{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__1. Exploratory Visualization__  \n",
    "__2. Data Cleaning__  \n",
    "__3. Feature Engineering__  \n",
    "__4. Modeling & Evaluation__  \n",
    "__5. Ensemble Methods__  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import RobustScaler, StandardScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from scipy.stats import skew\n",
    "from sklearn.decomposition import PCA, KernelPCA\n",
    "from sklearn.preprocessing import Imputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\MOOC\\Anaconda\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score, GridSearchCV, KFold\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, ExtraTreesRegressor\n",
    "from sklearn.svm import SVR, LinearSVR\n",
    "from sklearn.linear_model import ElasticNet, SGDRegressor, BayesianRidge\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('max_colwidth',200)\n",
    "pd.set_option('display.width',200)\n",
    "pd.set_option('display.max_columns',500)\n",
    "pd.set_option('display.max_rows',1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.read_csv('E:/Workspace/HousePrices/train.csv')\n",
    "test=pd.read_csv('E:/Workspace/HousePrices/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train = pd.read_csv('../input/train.csv')\n",
    "# test = pd.read_csv('../input/test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ __It seems that the price of recent-built houses are higher. So later I 'll use labelencoder for three \"Year\" feature.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x729c7b1dd8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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KzwEAAADJZazPmHwGtd71XuOsrCxdHuXrCwsL9YlPfCImaH7iE5+w/Ps0ZtDc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\nppp8UxTdqSHHvUumBuDSlWKr5JvbuRR/XVwKvQ8vrkgk9h+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PJcTIdU9jKuV3uriuZwMp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\noVAoFArFbeV/ASpdWnvtxjJMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x729c7b1390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.boxplot(train.YearBuilt, train.SalePrice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ __As is discussed in other kernels, the bottom right two two points with extremely large GrLivArea are likely to be outliers. So we delete them.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 800000)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P8qlPfYry8nLq6+sBqK+vZ9GiRQCUl5dz8OBB+vr6uHDhAufPn2fOnDnk5uaSmZnJiRMn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\nV29t91qw7LHZ0x20W0ukBq6N7TXKng4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q2LcX3n07+EPLeG1PaTe5ONrSplhJ9O+AiMgo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\nEZHBMWgnIiIiIjI4Bu1ERERERAb3/wHNNZYOqMdVHwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x729e071dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.scatter(x=train.GrLivArea, y=train.SalePrice)\n",
    "plt.xlabel(\"GrLivArea\", fontsize=13)\n",
    "plt.ylabel(\"SalePrice\", fontsize=13)\n",
    "plt.ylim(0,800000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(train[(train[\"GrLivArea\"]>4000)&(train[\"SalePrice\"]<300000)].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "full=pd.concat([train,test], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2917, 80)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full.drop(['Id'],axis=1, inplace=True)\n",
    "full.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PoolQC          2908\n",
       "MiscFeature     2812\n",
       "Alley           2719\n",
       "Fence           2346\n",
       "SalePrice       1459\n",
       "FireplaceQu     1420\n",
       "LotFrontage      486\n",
       "GarageQual       159\n",
       "GarageCond       159\n",
       "GarageFinish     159\n",
       "GarageYrBlt      159\n",
       "GarageType       157\n",
       "BsmtExposure      82\n",
       "BsmtCond          82\n",
       "BsmtQual          81\n",
       "BsmtFinType2      80\n",
       "BsmtFinType1      79\n",
       "MasVnrType        24\n",
       "MasVnrArea        23\n",
       "MSZoning           4\n",
       "BsmtFullBath       2\n",
       "BsmtHalfBath       2\n",
       "Utilities          2\n",
       "Functional         2\n",
       "Electrical         1\n",
       "BsmtUnfSF          1\n",
       "Exterior1st        1\n",
       "Exterior2nd        1\n",
       "TotalBsmtSF        1\n",
       "GarageCars         1\n",
       "BsmtFinSF2         1\n",
       "BsmtFinSF1         1\n",
       "KitchenQual        1\n",
       "SaleType           1\n",
       "GarageArea         1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aa = full.isnull().sum()\n",
    "aa[aa>0].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ __Let's first imput the missing values of LotFrontage based on the median of LotArea and Neighborhood. Since LotArea is a continuous feature, We use qcut to divide it into 10 parts.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">LotFrontage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Blmngtn</th>\n",
       "      <td>46.900000</td>\n",
       "      <td>43.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blueste</th>\n",
       "      <td>27.300000</td>\n",
       "      <td>24.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BrDale</th>\n",
       "      <td>21.500000</td>\n",
       "      <td>21.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BrkSide</th>\n",
       "      <td>55.789474</td>\n",
       "      <td>51.0</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ClearCr</th>\n",
       "      <td>88.150000</td>\n",
       "      <td>80.5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CollgCr</th>\n",
       "      <td>71.336364</td>\n",
       "      <td>70.0</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crawfor</th>\n",
       "      <td>69.951807</td>\n",
       "      <td>70.0</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edwards</th>\n",
       "      <td>65.153409</td>\n",
       "      <td>64.5</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gilbert</th>\n",
       "      <td>74.207207</td>\n",
       "      <td>64.0</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IDOTRR</th>\n",
       "      <td>62.241379</td>\n",
       "      <td>60.0</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MeadowV</th>\n",
       "      <td>25.606061</td>\n",
       "      <td>21.0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mitchel</th>\n",
       "      <td>75.144444</td>\n",
       "      <td>74.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAmes</th>\n",
       "      <td>75.210667</td>\n",
       "      <td>73.0</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NPkVill</th>\n",
       "      <td>28.142857</td>\n",
       "      <td>24.0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NWAmes</th>\n",
       "      <td>81.517647</td>\n",
       "      <td>80.0</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NoRidge</th>\n",
       "      <td>91.629630</td>\n",
       "      <td>89.0</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NridgHt</th>\n",
       "      <td>84.184049</td>\n",
       "      <td>92.0</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OldTown</th>\n",
       "      <td>61.777293</td>\n",
       "      <td>60.0</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SWISU</th>\n",
       "      <td>59.068182</td>\n",
       "      <td>60.0</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sawyer</th>\n",
       "      <td>74.551020</td>\n",
       "      <td>72.0</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SawyerW</th>\n",
       "      <td>70.669811</td>\n",
       "      <td>67.0</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Somerst</th>\n",
       "      <td>64.549383</td>\n",
       "      <td>72.5</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StoneBr</th>\n",
       "      <td>62.173913</td>\n",
       "      <td>60.0</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timber</th>\n",
       "      <td>81.157895</td>\n",
       "      <td>82.0</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Veenker</th>\n",
       "      <td>72.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             LotFrontage             \n",
       "                    mean median count\n",
       "Neighborhood                         \n",
       "Blmngtn        46.900000   43.0    20\n",
       "Blueste        27.300000   24.0    10\n",
       "BrDale         21.500000   21.0    30\n",
       "BrkSide        55.789474   51.0    95\n",
       "ClearCr        88.150000   80.5    20\n",
       "CollgCr        71.336364   70.0   220\n",
       "Crawfor        69.951807   70.0    83\n",
       "Edwards        65.153409   64.5   176\n",
       "Gilbert        74.207207   64.0   111\n",
       "IDOTRR         62.241379   60.0    87\n",
       "MeadowV        25.606061   21.0    33\n",
       "Mitchel        75.144444   74.0    90\n",
       "NAmes          75.210667   73.0   375\n",
       "NPkVill        28.142857   24.0    21\n",
       "NWAmes         81.517647   80.0    85\n",
       "NoRidge        91.629630   89.0    54\n",
       "NridgHt        84.184049   92.0   163\n",
       "OldTown        61.777293   60.0   229\n",
       "SWISU          59.068182   60.0    44\n",
       "Sawyer         74.551020   72.0    98\n",
       "SawyerW        70.669811   67.0   106\n",
       "Somerst        64.549383   72.5   162\n",
       "StoneBr        62.173913   60.0    46\n",
       "Timber         81.157895   82.0    57\n",
       "Veenker        72.000000   80.0    16"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full.groupby(['Neighborhood'])[['LotFrontage']].agg(['mean','median','count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "full[\"LotAreaCut\"] = pd.qcut(full.LotArea,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">LotFrontage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotAreaCut</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(1299.999, 4921.8]</th>\n",
       "      <td>35.741036</td>\n",
       "      <td>34.0</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(4921.8, 7007.2]</th>\n",
       "      <td>55.460674</td>\n",
       "      <td>52.0</td>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(7007.2, 7949.0]</th>\n",
       "      <td>62.959839</td>\n",
       "      <td>62.0</td>\n",
       "      <td>249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(7949.0, 8740.4]</th>\n",
       "      <td>67.113725</td>\n",
       "      <td>65.0</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(8740.4, 9452.0]</th>\n",
       "      <td>69.959184</td>\n",
       "      <td>70.0</td>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(9452.0, 10148.8]</th>\n",
       "      <td>73.988235</td>\n",
       "      <td>75.0</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(10148.8, 11000.0]</th>\n",
       "      <td>73.636364</td>\n",
       "      <td>75.0</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(11000.0, 12196.8]</th>\n",
       "      <td>83.371681</td>\n",
       "      <td>82.0</td>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(12196.8, 14285.8]</th>\n",
       "      <td>84.973684</td>\n",
       "      <td>85.0</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(14285.8, 215245.0]</th>\n",
       "      <td>92.846535</td>\n",
       "      <td>90.0</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    LotFrontage             \n",
       "                           mean median count\n",
       "LotAreaCut                                  \n",
       "(1299.999, 4921.8]    35.741036   34.0   251\n",
       "(4921.8, 7007.2]      55.460674   52.0   267\n",
       "(7007.2, 7949.0]      62.959839   62.0   249\n",
       "(7949.0, 8740.4]      67.113725   65.0   255\n",
       "(8740.4, 9452.0]      69.959184   70.0   245\n",
       "(9452.0, 10148.8]     73.988235   75.0   255\n",
       "(10148.8, 11000.0]    73.636364   75.0   253\n",
       "(11000.0, 12196.8]    83.371681   82.0   226\n",
       "(12196.8, 14285.8]    84.973684   85.0   228\n",
       "(14285.8, 215245.0]   92.846535   90.0   202"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full.groupby(['LotAreaCut'])[['LotFrontage']].agg(['mean','median','count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "full['LotFrontage']=full.groupby(['LotAreaCut','Neighborhood'])['LotFrontage'].transform(lambda x: x.fillna(x.median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since some combinations of LotArea and Neighborhood are not available, so we just LotAreaCut alone.\n",
    "full['LotFrontage']=full.groupby(['LotAreaCut'])['LotFrontage'].transform(lambda x: x.fillna(x.median()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ __Then we filling in other missing values according to data_description.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=[\"MasVnrArea\", \"BsmtUnfSF\", \"TotalBsmtSF\", \"GarageCars\", \"BsmtFinSF2\", \"BsmtFinSF1\", \"GarageArea\"]\n",
    "for col in cols:\n",
    "    full[col].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols1 = [\"PoolQC\" , \"MiscFeature\", \"Alley\", \"Fence\", \"FireplaceQu\", \"GarageQual\", \"GarageCond\", \"GarageFinish\", \"GarageYrBlt\", \"GarageType\", \"BsmtExposure\", \"BsmtCond\", \"BsmtQual\", \"BsmtFinType2\", \"BsmtFinType1\", \"MasVnrType\"]\n",
    "for col in cols1:\n",
    "    full[col].fillna(\"None\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill in with mode\n",
    "cols2 = [\"MSZoning\", \"BsmtFullBath\", \"BsmtHalfBath\", \"Utilities\", \"Functional\", \"Electrical\", \"KitchenQual\", \"SaleType\",\"Exterior1st\", \"Exterior2nd\"]\n",
    "for col in cols2:\n",
    "    full[col].fillna(full[col].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ __And there is no missing data except for the value we want to predict !__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalePrice    1459\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full.isnull().sum()[full.isnull().sum()>0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ __Convert some numerical features into categorical features. It's better to use LabelEncoder and get_dummies for these features.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "NumStr = [\"MSSubClass\",\"BsmtFullBath\",\"BsmtHalfBath\",\"HalfBath\",\"BedroomAbvGr\",\"KitchenAbvGr\",\"MoSold\",\"YrSold\",\"YearBuilt\",\"YearRemodAdd\",\"LowQualFinSF\",\"GarageYrBlt\"]\n",
    "for col in NumStr:\n",
    "    full[col]=full[col].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ __Now I want to do a long list of value-mapping. __\n",
    "+ __I was influenced by the insight that we should build as many features as possible and trust the model to choose the right features. So I decided to groupby SalePrice according to one feature and sort it based on mean and median. Here is an example:__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">SalePrice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>200779.080460</td>\n",
       "      <td>192000.0</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>138647.380952</td>\n",
       "      <td>146000.0</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>102300.000000</td>\n",
       "      <td>88500.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>129613.333333</td>\n",
       "      <td>128250.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>185224.811567</td>\n",
       "      <td>159250.0</td>\n",
       "      <td>536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>95829.724638</td>\n",
       "      <td>99900.0</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>156125.000000</td>\n",
       "      <td>142500.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>108591.666667</td>\n",
       "      <td>107500.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>143302.972222</td>\n",
       "      <td>132000.0</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>240403.542088</td>\n",
       "      <td>216000.0</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>166772.416667</td>\n",
       "      <td>156000.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>192437.500000</td>\n",
       "      <td>163500.0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>169736.551724</td>\n",
       "      <td>166500.0</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>147810.000000</td>\n",
       "      <td>140750.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>133541.076923</td>\n",
       "      <td>135980.0</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                SalePrice                \n",
       "                     mean    median count\n",
       "MSSubClass                               \n",
       "120         200779.080460  192000.0    87\n",
       "150                   NaN       NaN     0\n",
       "160         138647.380952  146000.0    63\n",
       "180         102300.000000   88500.0    10\n",
       "190         129613.333333  128250.0    30\n",
       "20          185224.811567  159250.0   536\n",
       "30           95829.724638   99900.0    69\n",
       "40          156125.000000  142500.0     4\n",
       "45          108591.666667  107500.0    12\n",
       "50          143302.972222  132000.0   144\n",
       "60          240403.542088  216000.0   297\n",
       "70          166772.416667  156000.0    60\n",
       "75          192437.500000  163500.0    16\n",
       "80          169736.551724  166500.0    58\n",
       "85          147810.000000  140750.0    20\n",
       "90          133541.076923  135980.0    52"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full.groupby(['MSSubClass'])[['SalePrice']].agg(['mean','median','count'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ __So basically I'll do__  \n",
    "                '180' : 1\n",
    "                '30' : 2   '45' : 2\n",
    "                '190' : 3, '50' : 3, '90' : 3,\n",
    "                '85' : 4, '40' : 4, '160' : 4\n",
    "                '70' : 5, '20' : 5, '75' : 5, '80' : 5, '150' : 5\n",
    "                '120': 6, '60' : 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ __Different people may have different views on how to map these values, so just follow your instinct =^_^=__  \n",
    "__Below I also add a small \"o\" in front of the features so as to keep the original features to use get_dummies in a moment.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_values():\n",
    "    full[\"oMSSubClass\"] = full.MSSubClass.map({'180':1, \n",
    "                                        '30':2, '45':2, \n",
    "                                        '190':3, '50':3, '90':3, \n",
    "                                        '85':4, '40':4, '160':4, \n",
    "                                        '70':5, '20':5, '75':5, '80':5, '150':5,\n",
    "                                        '120': 6, '60':6})\n",
    "    \n",
    "    full[\"oMSZoning\"] = full.MSZoning.map({'C (all)':1, 'RH':2, 'RM':2, 'RL':3, 'FV':4})\n",
    "    \n",
    "    full[\"oNeighborhood\"] = full.Neighborhood.map({'MeadowV':1,\n",
    "                                               'IDOTRR':2, 'BrDale':2,\n",
    "                                               'OldTown':3, 'Edwards':3, 'BrkSide':3,\n",
    "                                               'Sawyer':4, 'Blueste':4, 'SWISU':4, 'NAmes':4,\n",
    "                                               'NPkVill':5, 'Mitchel':5,\n",
    "                                               'SawyerW':6, 'Gilbert':6, 'NWAmes':6,\n",
    "                                               'Blmngtn':7, 'CollgCr':7, 'ClearCr':7, 'Crawfor':7,\n",
    "                                               'Veenker':8, 'Somerst':8, 'Timber':8,\n",
    "                                               'StoneBr':9,\n",
    "                                               'NoRidge':10, 'NridgHt':10})\n",
    "    \n",
    "    full[\"oCondition1\"] = full.Condition1.map({'Artery':1,\n",
    "                                           'Feedr':2, 'RRAe':2,\n",
    "                                           'Norm':3, 'RRAn':3,\n",
    "                                           'PosN':4, 'RRNe':4,\n",
    "                                           'PosA':5 ,'RRNn':5})\n",
    "    \n",
    "    full[\"oBldgType\"] = full.BldgType.map({'2fmCon':1, 'Duplex':1, 'Twnhs':1, '1Fam':2, 'TwnhsE':2})\n",
    "    \n",
    "    full[\"oHouseStyle\"] = full.HouseStyle.map({'1.5Unf':1, \n",
    "                                           '1.5Fin':2, '2.5Unf':2, 'SFoyer':2, \n",
    "                                           '1Story':3, 'SLvl':3,\n",
    "                                           '2Story':4, '2.5Fin':4})\n",
    "    \n",
    "    full[\"oExterior1st\"] = full.Exterior1st.map({'BrkComm':1,\n",
    "                                             'AsphShn':2, 'CBlock':2, 'AsbShng':2,\n",
    "                                             'WdShing':3, 'Wd Sdng':3, 'MetalSd':3, 'Stucco':3, 'HdBoard':3,\n",
    "                                             'BrkFace':4, 'Plywood':4,\n",
    "                                             'VinylSd':5,\n",
    "                                             'CemntBd':6,\n",
    "                                             'Stone':7, 'ImStucc':7})\n",
    "    \n",
    "    full[\"oMasVnrType\"] = full.MasVnrType.map({'BrkCmn':1, 'None':1, 'BrkFace':2, 'Stone':3})\n",
    "    \n",
    "    full[\"oExterQual\"] = full.ExterQual.map({'Fa':1, 'TA':2, 'Gd':3, 'Ex':4})\n",
    "    \n",
    "    full[\"oFoundation\"] = full.Foundation.map({'Slab':1, \n",
    "                                           'BrkTil':2, 'CBlock':2, 'Stone':2,\n",
    "                                           'Wood':3, 'PConc':4})\n",
    "    \n",
    "    full[\"oBsmtQual\"] = full.BsmtQual.map({'Fa':2, 'None':1, 'TA':3, 'Gd':4, 'Ex':5})\n",
    "    \n",
    "    full[\"oBsmtExposure\"] = full.BsmtExposure.map({'None':1, 'No':2, 'Av':3, 'Mn':3, 'Gd':4})\n",
    "    \n",
    "    full[\"oHeating\"] = full.Heating.map({'Floor':1, 'Grav':1, 'Wall':2, 'OthW':3, 'GasW':4, 'GasA':5})\n",
    "    \n",
    "    full[\"oHeatingQC\"] = full.HeatingQC.map({'Po':1, 'Fa':2, 'TA':3, 'Gd':4, 'Ex':5})\n",
    "    \n",
    "    full[\"oKitchenQual\"] = full.KitchenQual.map({'Fa':1, 'TA':2, 'Gd':3, 'Ex':4})\n",
    "    \n",
    "    full[\"oFunctional\"] = full.Functional.map({'Maj2':1, 'Maj1':2, 'Min1':2, 'Min2':2, 'Mod':2, 'Sev':2, 'Typ':3})\n",
    "    \n",
    "    full[\"oFireplaceQu\"] = full.FireplaceQu.map({'None':1, 'Po':1, 'Fa':2, 'TA':3, 'Gd':4, 'Ex':5})\n",
    "    \n",
    "    full[\"oGarageType\"] = full.GarageType.map({'CarPort':1, 'None':1,\n",
    "                                           'Detchd':2,\n",
    "                                           '2Types':3, 'Basment':3,\n",
    "                                           'Attchd':4, 'BuiltIn':5})\n",
    "    \n",
    "    full[\"oGarageFinish\"] = full.GarageFinish.map({'None':1, 'Unf':2, 'RFn':3, 'Fin':4})\n",
    "    \n",
    "    full[\"oPavedDrive\"] = full.PavedDrive.map({'N':1, 'P':2, 'Y':3})\n",
    "    \n",
    "    full[\"oSaleType\"] = full.SaleType.map({'COD':1, 'ConLD':1, 'ConLI':1, 'ConLw':1, 'Oth':1, 'WD':1,\n",
    "                                       'CWD':2, 'Con':3, 'New':3})\n",
    "    \n",
    "    full[\"oSaleCondition\"] = full.SaleCondition.map({'AdjLand':1, 'Abnorml':2, 'Alloca':2, 'Family':2, 'Normal':3, 'Partial':4})            \n",
    "                \n",
    "                        \n",
    "                        \n",
    "    \n",
    "    return \"Done!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Done!'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop two unwanted columns\n",
    "full.drop(\"LotAreaCut\",axis=1,inplace=True)\n",
    "full.drop(['SalePrice'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ __Next we can build a pipeline. It's convenient to experiment different feature combinations once you've got a pipeline.__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ __Label Encoding three \"Year\" features.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "class labelenc(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    \n",
    "    def fit(self,X,y=None):\n",
    "        return self\n",
    "    \n",
    "    def transform(self,X):\n",
    "        lab=LabelEncoder()\n",
    "        X[\"YearBuilt\"] = lab.fit_transform(X[\"YearBuilt\"])\n",
    "        X[\"YearRemodAdd\"] = lab.fit_transform(X[\"YearRemodAdd\"])\n",
    "        X[\"GarageYrBlt\"] = lab.fit_transform(X[\"GarageYrBlt\"])\n",
    "        return X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ __Apply log1p to the skewed features, then get_dummies.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "class skew_dummies(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self,skew=0.5):\n",
    "        self.skew = skew\n",
    "    \n",
    "    def fit(self,X,y=None):\n",
    "        return self\n",
    "    \n",
    "    def transform(self,X):\n",
    "        X_numeric=X.select_dtypes(exclude=[\"object\"])\n",
    "        skewness = X_numeric.apply(lambda x: skew(x))\n",
    "        skewness_features = skewness[abs(skewness) >= self.skew].index\n",
    "        X[skewness_features] = np.log1p(X[skewness_features])\n",
    "        X = pd.get_dummies(X)\n",
    "        return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build pipeline\n",
    "pipe = Pipeline([\n",
    "    ('labenc', labelenc()),\n",
    "    ('skew_dummies', skew_dummies(skew=1)),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the original data for later use\n",
    "full2 = full.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pipe = pipe.fit_transform(full2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2917, 405)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pipe.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>oMSSubClass</th>\n",
       "      <th>oMSZoning</th>\n",
       "      <th>oNeighborhood</th>\n",
       "      <th>oCondition1</th>\n",
       "      <th>oBldgType</th>\n",
       "      <th>oHouseStyle</th>\n",
       "      <th>oExterior1st</th>\n",
       "      <th>oMasVnrType</th>\n",
       "      <th>oExterQual</th>\n",
       "      <th>oFoundation</th>\n",
       "      <th>oBsmtQual</th>\n",
       "      <th>oBsmtExposure</th>\n",
       "      <th>oHeating</th>\n",
       "      <th>oHeatingQC</th>\n",
       "      <th>oKitchenQual</th>\n",
       "      <th>oFunctional</th>\n",
       "      <th>oFireplaceQu</th>\n",
       "      <th>oGarageType</th>\n",
       "      <th>oGarageFinish</th>\n",
       "      <th>oPavedDrive</th>\n",
       "      <th>oSaleType</th>\n",
       "      <th>oSaleCondition</th>\n",
       "      <th>Alley_Grvl</th>\n",
       "      <th>Alley_None</th>\n",
       "      <th>Alley_Pave</th>\n",
       "      <th>BedroomAbvGr_0</th>\n",
       "      <th>BedroomAbvGr_1</th>\n",
       "      <th>BedroomAbvGr_2</th>\n",
       "      <th>BedroomAbvGr_3</th>\n",
       "      <th>BedroomAbvGr_4</th>\n",
       "      <th>BedroomAbvGr_5</th>\n",
       "      <th>BedroomAbvGr_6</th>\n",
       "      <th>BedroomAbvGr_8</th>\n",
       "      <th>BldgType_1Fam</th>\n",
       "      <th>BldgType_2fmCon</th>\n",
       "      <th>BldgType_Duplex</th>\n",
       "      <th>BldgType_Twnhs</th>\n",
       "      <th>BldgType_TwnhsE</th>\n",
       "      <th>BsmtCond_Fa</th>\n",
       "      <th>BsmtCond_Gd</th>\n",
       "      <th>BsmtCond_None</th>\n",
       "      <th>BsmtCond_Po</th>\n",
       "      <th>BsmtCond_TA</th>\n",
       "      <th>BsmtExposure_Av</th>\n",
       "      <th>BsmtExposure_Gd</th>\n",
       "      <th>BsmtExposure_Mn</th>\n",
       "      <th>BsmtExposure_No</th>\n",
       "      <th>BsmtExposure_None</th>\n",
       "      <th>BsmtFinType1_ALQ</th>\n",
       "      <th>BsmtFinType1_BLQ</th>\n",
       "      <th>BsmtFinType1_GLQ</th>\n",
       "      <th>BsmtFinType1_LwQ</th>\n",
       "      <th>BsmtFinType1_None</th>\n",
       "      <th>BsmtFinType1_Rec</th>\n",
       "      <th>BsmtFinType1_Unf</th>\n",
       "      <th>BsmtFinType2_ALQ</th>\n",
       "      <th>BsmtFinType2_BLQ</th>\n",
       "      <th>BsmtFinType2_GLQ</th>\n",
       "      <th>BsmtFinType2_LwQ</th>\n",
       "      <th>BsmtFinType2_None</th>\n",
       "      <th>BsmtFinType2_Rec</th>\n",
       "      <th>BsmtFinType2_Unf</th>\n",
       "      <th>BsmtFullBath_0.0</th>\n",
       "      <th>BsmtFullBath_1.0</th>\n",
       "      <th>BsmtFullBath_2.0</th>\n",
       "      <th>BsmtFullBath_3.0</th>\n",
       "      <th>BsmtHalfBath_0.0</th>\n",
       "      <th>BsmtHalfBath_1.0</th>\n",
       "      <th>BsmtHalfBath_2.0</th>\n",
       "      <th>BsmtQual_Ex</th>\n",
       "      <th>BsmtQual_Fa</th>\n",
       "      <th>BsmtQual_Gd</th>\n",
       "      <th>BsmtQual_None</th>\n",
       "      <th>BsmtQual_TA</th>\n",
       "      <th>CentralAir_N</th>\n",
       "      <th>CentralAir_Y</th>\n",
       "      <th>Condition1_Artery</th>\n",
       "      <th>Condition1_Feedr</th>\n",
       "      <th>Condition1_Norm</th>\n",
       "      <th>Condition1_PosA</th>\n",
       "      <th>Condition1_PosN</th>\n",
       "      <th>Condition1_RRAe</th>\n",
       "      <th>Condition1_RRAn</th>\n",
       "      <th>Condition1_RRNe</th>\n",
       "      <th>Condition1_RRNn</th>\n",
       "      <th>Condition2_Artery</th>\n",
       "      <th>Condition2_Feedr</th>\n",
       "      <th>Condition2_Norm</th>\n",
       "      <th>Condition2_PosA</th>\n",
       "      <th>Condition2_PosN</th>\n",
       "      <th>Condition2_RRAe</th>\n",
       "      <th>Condition2_RRAn</th>\n",
       "      <th>Condition2_RRNn</th>\n",
       "      <th>Electrical_FuseA</th>\n",
       "      <th>Electrical_FuseF</th>\n",
       "      <th>Electrical_FuseP</th>\n",
       "      <th>Electrical_Mix</th>\n",
       "      <th>Electrical_SBrkr</th>\n",
       "      <th>ExterCond_Ex</th>\n",
       "      <th>ExterCond_Fa</th>\n",
       "      <th>ExterCond_Gd</th>\n",
       "      <th>ExterCond_Po</th>\n",
       "      <th>ExterCond_TA</th>\n",
       "      <th>ExterQual_Ex</th>\n",
       "      <th>ExterQual_Fa</th>\n",
       "      <th>ExterQual_Gd</th>\n",
       "      <th>ExterQual_TA</th>\n",
       "      <th>Exterior1st_AsbShng</th>\n",
       "      <th>Exterior1st_AsphShn</th>\n",
       "      <th>Exterior1st_BrkComm</th>\n",
       "      <th>Exterior1st_BrkFace</th>\n",
       "      <th>Exterior1st_CBlock</th>\n",
       "      <th>Exterior1st_CemntBd</th>\n",
       "      <th>Exterior1st_HdBoard</th>\n",
       "      <th>Exterior1st_ImStucc</th>\n",
       "      <th>Exterior1st_MetalSd</th>\n",
       "      <th>Exterior1st_Plywood</th>\n",
       "      <th>Exterior1st_Stone</th>\n",
       "      <th>Exterior1st_Stucco</th>\n",
       "      <th>Exterior1st_VinylSd</th>\n",
       "      <th>Exterior1st_Wd Sdng</th>\n",
       "      <th>Exterior1st_WdShing</th>\n",
       "      <th>Exterior2nd_AsbShng</th>\n",
       "      <th>Exterior2nd_AsphShn</th>\n",
       "      <th>Exterior2nd_Brk Cmn</th>\n",
       "      <th>Exterior2nd_BrkFace</th>\n",
       "      <th>Exterior2nd_CBlock</th>\n",
       "      <th>Exterior2nd_CmentBd</th>\n",
       "      <th>Exterior2nd_HdBoard</th>\n",
       "      <th>Exterior2nd_ImStucc</th>\n",
       "      <th>Exterior2nd_MetalSd</th>\n",
       "      <th>Exterior2nd_Other</th>\n",
       "      <th>Exterior2nd_Plywood</th>\n",
       "      <th>Exterior2nd_Stone</th>\n",
       "      <th>Exterior2nd_Stucco</th>\n",
       "      <th>Exterior2nd_VinylSd</th>\n",
       "      <th>Exterior2nd_Wd Sdng</th>\n",
       "      <th>Exterior2nd_Wd Shng</th>\n",
       "      <th>Fence_GdPrv</th>\n",
       "      <th>Fence_GdWo</th>\n",
       "      <th>Fence_MnPrv</th>\n",
       "      <th>Fence_MnWw</th>\n",
       "      <th>Fence_None</th>\n",
       "      <th>FireplaceQu_Ex</th>\n",
       "      <th>FireplaceQu_Fa</th>\n",
       "      <th>FireplaceQu_Gd</th>\n",
       "      <th>FireplaceQu_None</th>\n",
       "      <th>FireplaceQu_Po</th>\n",
       "      <th>FireplaceQu_TA</th>\n",
       "      <th>Foundation_BrkTil</th>\n",
       "      <th>Foundation_CBlock</th>\n",
       "      <th>Foundation_PConc</th>\n",
       "      <th>Foundation_Slab</th>\n",
       "      <th>Foundation_Stone</th>\n",
       "      <th>Foundation_Wood</th>\n",
       "      <th>Functional_Maj1</th>\n",
       "      <th>Functional_Maj2</th>\n",
       "      <th>Functional_Min1</th>\n",
       "      <th>Functional_Min2</th>\n",
       "      <th>Functional_Mod</th>\n",
       "      <th>Functional_Sev</th>\n",
       "      <th>Functional_Typ</th>\n",
       "      <th>GarageCond_Ex</th>\n",
       "      <th>GarageCond_Fa</th>\n",
       "      <th>GarageCond_Gd</th>\n",
       "      <th>GarageCond_None</th>\n",
       "      <th>GarageCond_Po</th>\n",
       "      <th>GarageCond_TA</th>\n",
       "      <th>GarageFinish_Fin</th>\n",
       "      <th>GarageFinish_None</th>\n",
       "      <th>GarageFinish_RFn</th>\n",
       "      <th>GarageFinish_Unf</th>\n",
       "      <th>GarageQual_Ex</th>\n",
       "      <th>GarageQual_Fa</th>\n",
       "      <th>GarageQual_Gd</th>\n",
       "      <th>GarageQual_None</th>\n",
       "      <th>GarageQual_Po</th>\n",
       "      <th>GarageQual_TA</th>\n",
       "      <th>GarageType_2Types</th>\n",
       "      <th>GarageType_Attchd</th>\n",
       "      <th>GarageType_Basment</th>\n",
       "      <th>GarageType_BuiltIn</th>\n",
       "      <th>GarageType_CarPort</th>\n",
       "      <th>GarageType_Detchd</th>\n",
       "      <th>GarageType_None</th>\n",
       "      <th>HalfBath_0</th>\n",
       "      <th>HalfBath_1</th>\n",
       "      <th>HalfBath_2</th>\n",
       "      <th>Heating_Floor</th>\n",
       "      <th>Heating_GasA</th>\n",
       "      <th>Heating_GasW</th>\n",
       "      <th>Heating_Grav</th>\n",
       "      <th>Heating_OthW</th>\n",
       "      <th>Heating_Wall</th>\n",
       "      <th>HeatingQC_Ex</th>\n",
       "      <th>HeatingQC_Fa</th>\n",
       "      <th>HeatingQC_Gd</th>\n",
       "      <th>HeatingQC_Po</th>\n",
       "      <th>HeatingQC_TA</th>\n",
       "      <th>HouseStyle_1.5Fin</th>\n",
       "      <th>HouseStyle_1.5Unf</th>\n",
       "      <th>HouseStyle_1Story</th>\n",
       "      <th>HouseStyle_2.5Fin</th>\n",
       "      <th>HouseStyle_2.5Unf</th>\n",
       "      <th>HouseStyle_2Story</th>\n",
       "      <th>HouseStyle_SFoyer</th>\n",
       "      <th>HouseStyle_SLvl</th>\n",
       "      <th>KitchenAbvGr_0</th>\n",
       "      <th>KitchenAbvGr_1</th>\n",
       "      <th>KitchenAbvGr_2</th>\n",
       "      <th>KitchenAbvGr_3</th>\n",
       "      <th>KitchenQual_Ex</th>\n",
       "      <th>KitchenQual_Fa</th>\n",
       "      <th>KitchenQual_Gd</th>\n",
       "      <th>KitchenQual_TA</th>\n",
       "      <th>LandContour_Bnk</th>\n",
       "      <th>LandContour_HLS</th>\n",
       "      <th>LandContour_Low</th>\n",
       "      <th>LandContour_Lvl</th>\n",
       "      <th>LandSlope_Gtl</th>\n",
       "      <th>LandSlope_Mod</th>\n",
       "      <th>LandSlope_Sev</th>\n",
       "      <th>LotConfig_Corner</th>\n",
       "      <th>LotConfig_CulDSac</th>\n",
       "      <th>LotConfig_FR2</th>\n",
       "      <th>LotConfig_FR3</th>\n",
       "      <th>LotConfig_Inside</th>\n",
       "      <th>LotShape_IR1</th>\n",
       "      <th>LotShape_IR2</th>\n",
       "      <th>LotShape_IR3</th>\n",
       "      <th>LotShape_Reg</th>\n",
       "      <th>LowQualFinSF_0</th>\n",
       "      <th>LowQualFinSF_1064</th>\n",
       "      <th>LowQualFinSF_108</th>\n",
       "      <th>LowQualFinSF_114</th>\n",
       "      <th>LowQualFinSF_120</th>\n",
       "      <th>LowQualFinSF_140</th>\n",
       "      <th>LowQualFinSF_144</th>\n",
       "      <th>LowQualFinSF_156</th>\n",
       "      <th>LowQualFinSF_205</th>\n",
       "      <th>LowQualFinSF_232</th>\n",
       "      <th>LowQualFinSF_234</th>\n",
       "      <th>LowQualFinSF_259</th>\n",
       "      <th>LowQualFinSF_312</th>\n",
       "      <th>LowQualFinSF_360</th>\n",
       "      <th>LowQualFinSF_362</th>\n",
       "      <th>LowQualFinSF_371</th>\n",
       "      <th>LowQualFinSF_384</th>\n",
       "      <th>LowQualFinSF_390</th>\n",
       "      <th>LowQualFinSF_392</th>\n",
       "      <th>LowQualFinSF_397</th>\n",
       "      <th>LowQualFinSF_420</th>\n",
       "      <th>LowQualFinSF_431</th>\n",
       "      <th>LowQualFinSF_436</th>\n",
       "      <th>LowQualFinSF_450</th>\n",
       "      <th>LowQualFinSF_473</th>\n",
       "      <th>LowQualFinSF_479</th>\n",
       "      <th>LowQualFinSF_481</th>\n",
       "      <th>LowQualFinSF_512</th>\n",
       "      <th>LowQualFinSF_513</th>\n",
       "      <th>LowQualFinSF_514</th>\n",
       "      <th>LowQualFinSF_515</th>\n",
       "      <th>LowQualFinSF_528</th>\n",
       "      <th>LowQualFinSF_53</th>\n",
       "      <th>LowQualFinSF_572</th>\n",
       "      <th>LowQualFinSF_697</th>\n",
       "      <th>LowQualFinSF_80</th>\n",
       "      <th>MSSubClass_120</th>\n",
       "      <th>MSSubClass_150</th>\n",
       "      <th>MSSubClass_160</th>\n",
       "      <th>MSSubClass_180</th>\n",
       "      <th>MSSubClass_190</th>\n",
       "      <th>MSSubClass_20</th>\n",
       "      <th>MSSubClass_30</th>\n",
       "      <th>MSSubClass_40</th>\n",
       "      <th>MSSubClass_45</th>\n",
       "      <th>MSSubClass_50</th>\n",
       "      <th>MSSubClass_60</th>\n",
       "      <th>MSSubClass_70</th>\n",
       "      <th>MSSubClass_75</th>\n",
       "      <th>MSSubClass_80</th>\n",
       "      <th>MSSubClass_85</th>\n",
       "      <th>MSSubClass_90</th>\n",
       "      <th>MSZoning_C (all)</th>\n",
       "      <th>MSZoning_FV</th>\n",
       "      <th>MSZoning_RH</th>\n",
       "      <th>MSZoning_RL</th>\n",
       "      <th>MSZoning_RM</th>\n",
       "      <th>MasVnrType_BrkCmn</th>\n",
       "      <th>MasVnrType_BrkFace</th>\n",
       "      <th>MasVnrType_None</th>\n",
       "      <th>MasVnrType_Stone</th>\n",
       "      <th>MiscFeature_Gar2</th>\n",
       "      <th>MiscFeature_None</th>\n",
       "      <th>MiscFeature_Othr</th>\n",
       "      <th>MiscFeature_Shed</th>\n",
       "      <th>MiscFeature_TenC</th>\n",
       "      <th>MoSold_1</th>\n",
       "      <th>MoSold_10</th>\n",
       "      <th>MoSold_11</th>\n",
       "      <th>MoSold_12</th>\n",
       "      <th>MoSold_2</th>\n",
       "      <th>MoSold_3</th>\n",
       "      <th>MoSold_4</th>\n",
       "      <th>MoSold_5</th>\n",
       "      <th>MoSold_6</th>\n",
       "      <th>MoSold_7</th>\n",
       "      <th>MoSold_8</th>\n",
       "      <th>MoSold_9</th>\n",
       "      <th>Neighborhood_Blmngtn</th>\n",
       "      <th>Neighborhood_Blueste</th>\n",
       "      <th>Neighborhood_BrDale</th>\n",
       "      <th>Neighborhood_BrkSide</th>\n",
       "      <th>Neighborhood_ClearCr</th>\n",
       "      <th>Neighborhood_CollgCr</th>\n",
       "      <th>Neighborhood_Crawfor</th>\n",
       "      <th>Neighborhood_Edwards</th>\n",
       "      <th>Neighborhood_Gilbert</th>\n",
       "      <th>Neighborhood_IDOTRR</th>\n",
       "      <th>Neighborhood_MeadowV</th>\n",
       "      <th>Neighborhood_Mitchel</th>\n",
       "      <th>Neighborhood_NAmes</th>\n",
       "      <th>Neighborhood_NPkVill</th>\n",
       "      <th>Neighborhood_NWAmes</th>\n",
       "      <th>Neighborhood_NoRidge</th>\n",
       "      <th>Neighborhood_NridgHt</th>\n",
       "      <th>Neighborhood_OldTown</th>\n",
       "      <th>Neighborhood_SWISU</th>\n",
       "      <th>Neighborhood_Sawyer</th>\n",
       "      <th>Neighborhood_SawyerW</th>\n",
       "      <th>Neighborhood_Somerst</th>\n",
       "      <th>Neighborhood_StoneBr</th>\n",
       "      <th>Neighborhood_Timber</th>\n",
       "      <th>Neighborhood_Veenker</th>\n",
       "      <th>PavedDrive_N</th>\n",
       "      <th>PavedDrive_P</th>\n",
       "      <th>PavedDrive_Y</th>\n",
       "      <th>PoolQC_Ex</th>\n",
       "      <th>PoolQC_Fa</th>\n",
       "      <th>PoolQC_Gd</th>\n",
       "      <th>PoolQC_None</th>\n",
       "      <th>RoofMatl_CompShg</th>\n",
       "      <th>RoofMatl_Membran</th>\n",
       "      <th>RoofMatl_Metal</th>\n",
       "      <th>RoofMatl_Roll</th>\n",
       "      <th>RoofMatl_Tar&amp;Grv</th>\n",
       "      <th>RoofMatl_WdShake</th>\n",
       "      <th>RoofMatl_WdShngl</th>\n",
       "      <th>RoofStyle_Flat</th>\n",
       "      <th>RoofStyle_Gable</th>\n",
       "      <th>RoofStyle_Gambrel</th>\n",
       "      <th>RoofStyle_Hip</th>\n",
       "      <th>RoofStyle_Mansard</th>\n",
       "      <th>RoofStyle_Shed</th>\n",
       "      <th>SaleCondition_Abnorml</th>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <th>SaleCondition_Alloca</th>\n",
       "      <th>SaleCondition_Family</th>\n",
       "      <th>SaleCondition_Normal</th>\n",
       "      <th>SaleCondition_Partial</th>\n",
       "      <th>SaleType_COD</th>\n",
       "      <th>SaleType_CWD</th>\n",
       "      <th>SaleType_Con</th>\n",
       "      <th>SaleType_ConLD</th>\n",
       "      <th>SaleType_ConLI</th>\n",
       "      <th>SaleType_ConLw</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>Street_Grvl</th>\n",
       "      <th>Street_Pave</th>\n",
       "      <th>Utilities_AllPub</th>\n",
       "      <th>Utilities_NoSeWa</th>\n",
       "      <th>YrSold_2006</th>\n",
       "      <th>YrSold_2007</th>\n",
       "      <th>YrSold_2008</th>\n",
       "      <th>YrSold_2009</th>\n",
       "      <th>YrSold_2010</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.753438</td>\n",
       "      <td>854</td>\n",
       "      <td>0.0</td>\n",
       "      <td>706.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>548.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>94</td>\n",
       "      <td>7.444833</td>\n",
       "      <td>9.042040</td>\n",
       "      <td>65.0</td>\n",
       "      <td>5.283204</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.127134</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>856.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>110</td>\n",
       "      <td>53</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.141245</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>460.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>67</td>\n",
       "      <td>7.141245</td>\n",
       "      <td>9.169623</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1262.0</td>\n",
       "      <td>5.700444</td>\n",
       "      <td>83</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.825460</td>\n",
       "      <td>866</td>\n",
       "      <td>0.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>608.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>92</td>\n",
       "      <td>7.488294</td>\n",
       "      <td>9.328212</td>\n",
       "      <td>68.0</td>\n",
       "      <td>5.093750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.761200</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>920.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>108</td>\n",
       "      <td>52</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.869014</td>\n",
       "      <td>756</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>5.609472</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>642.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>89</td>\n",
       "      <td>7.448916</td>\n",
       "      <td>9.164401</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.583519</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>756.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.044033</td>\n",
       "      <td>1053</td>\n",
       "      <td>0.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>836.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>91</td>\n",
       "      <td>7.695758</td>\n",
       "      <td>9.565284</td>\n",
       "      <td>84.0</td>\n",
       "      <td>5.860786</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.442651</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>5.262690</td>\n",
       "      <td>107</td>\n",
       "      <td>50</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   1stFlrSF  2ndFlrSF  3SsnPorch  BsmtFinSF1  BsmtFinSF2  BsmtUnfSF  EnclosedPorch  Fireplaces  FullBath  GarageArea  GarageCars  GarageYrBlt  GrLivArea   LotArea  LotFrontage  MasVnrArea  MiscVal  \\\n",
       "0  6.753438       854        0.0       706.0         0.0      150.0       0.000000           0         2       548.0         2.0           94   7.444833  9.042040         65.0    5.283204      0.0   \n",
       "1  7.141245         0        0.0       978.0         0.0      284.0       0.000000           1         2       460.0         2.0           67   7.141245  9.169623         80.0    0.000000      0.0   \n",
       "2  6.825460       866        0.0       486.0         0.0      434.0       0.000000           1         2       608.0         2.0           92   7.488294  9.328212         68.0    5.093750      0.0   \n",
       "3  6.869014       756        0.0       216.0         0.0      540.0       5.609472           1         1       642.0         3.0           89   7.448916  9.164401         60.0    0.000000      0.0   \n",
       "4  7.044033      1053        0.0       655.0         0.0      490.0       0.000000           1         2       836.0         3.0           91   7.695758  9.565284         84.0    5.860786      0.0   \n",
       "\n",
       "   OpenPorchSF  OverallCond  OverallQual  PoolArea  ScreenPorch  TotRmsAbvGrd  TotalBsmtSF  WoodDeckSF  YearBuilt  YearRemodAdd  oMSSubClass  oMSZoning  oNeighborhood  oCondition1  oBldgType  \\\n",
       "0     4.127134            5            7       0.0          0.0             8        856.0    0.000000        110            53            6          3              7     1.386294   1.098612   \n",
       "1     0.000000            8            6       0.0          0.0             6       1262.0    5.700444         83            26            5          3              8     1.098612   1.098612   \n",
       "2     3.761200            5            7       0.0          0.0             6        920.0    0.000000        108            52            6          3              7     1.386294   1.098612   \n",
       "3     3.583519            5            7       0.0          0.0             7        756.0    0.000000         25            20            5          3              7     1.386294   1.098612   \n",
       "4     4.442651            5            8       0.0          0.0             9       1145.0    5.262690        107            50            6          3             10     1.386294   1.098612   \n",
       "\n",
       "   oHouseStyle  oExterior1st  oMasVnrType  oExterQual  oFoundation  oBsmtQual  oBsmtExposure  oHeating  oHeatingQC  oKitchenQual  oFunctional  oFireplaceQu  oGarageType  oGarageFinish  oPavedDrive  \\\n",
       "0            4             5     1.098612           3            4          4       1.098612  1.791759           5             3     1.386294             1            4              3     1.386294   \n",
       "1            3             3     0.693147           2            2          4       1.609438  1.791759           5             2     1.386294             3            4              3     1.386294   \n",
       "2            4             5     1.098612           3            4          4       1.386294  1.791759           5             3     1.386294             3            4              3     1.386294   \n",
       "3            4             3     0.693147           2            2          3       1.098612  1.791759           4             3     1.386294             4            2              2     1.386294   \n",
       "4            4             5     1.098612           3            4          4       1.386294  1.791759           5             3     1.386294             3            4              3     1.386294   \n",
       "\n",
       "   oSaleType  oSaleCondition  Alley_Grvl  Alley_None  Alley_Pave  BedroomAbvGr_0  BedroomAbvGr_1  BedroomAbvGr_2  BedroomAbvGr_3  BedroomAbvGr_4  BedroomAbvGr_5  BedroomAbvGr_6  BedroomAbvGr_8  \\\n",
       "0   0.693147               3           0           1           0               0               0               0               1               0               0               0               0   \n",
       "1   0.693147               3           0           1           0               0               0               0               1               0               0               0               0   \n",
       "2   0.693147               3           0           1           0               0               0               0               1               0               0               0               0   \n",
       "3   0.693147               2           0           1           0               0               0               0               1               0               0               0               0   \n",
       "4   0.693147               3           0           1           0               0               0               0               0               1               0               0               0   \n",
       "\n",
       "   BldgType_1Fam  BldgType_2fmCon  BldgType_Duplex  BldgType_Twnhs  BldgType_TwnhsE  BsmtCond_Fa  BsmtCond_Gd  BsmtCond_None  BsmtCond_Po  BsmtCond_TA  BsmtExposure_Av  BsmtExposure_Gd  \\\n",
       "0              1                0                0               0                0            0            0              0            0            1                0                0   \n",
       "1              1                0                0               0                0            0            0              0            0            1                0                1   \n",
       "2              1                0                0               0                0            0            0              0            0            1                0                0   \n",
       "3              1                0                0               0                0            0            1              0            0            0                0                0   \n",
       "4              1                0                0               0                0            0            0              0            0            1                1                0   \n",
       "\n",
       "   BsmtExposure_Mn  BsmtExposure_No  BsmtExposure_None  BsmtFinType1_ALQ  BsmtFinType1_BLQ  BsmtFinType1_GLQ  BsmtFinType1_LwQ  BsmtFinType1_None  BsmtFinType1_Rec  BsmtFinType1_Unf  \\\n",
       "0                0                1                  0                 0                 0                 1                 0                  0                 0                 0   \n",
       "1                0                0                  0                 1                 0                 0                 0                  0                 0                 0   \n",
       "2                1                0                  0                 0                 0                 1                 0                  0                 0                 0   \n",
       "3                0                1                  0                 1                 0                 0                 0                  0                 0                 0   \n",
       "4                0                0                  0                 0                 0                 1                 0                  0                 0                 0   \n",
       "\n",
       "   BsmtFinType2_ALQ  BsmtFinType2_BLQ  BsmtFinType2_GLQ  BsmtFinType2_LwQ  BsmtFinType2_None  BsmtFinType2_Rec  BsmtFinType2_Unf  BsmtFullBath_0.0  BsmtFullBath_1.0  BsmtFullBath_2.0  \\\n",
       "0                 0                 0                 0                 0                  0                 0                 1                 0                 1                 0   \n",
       "1                 0                 0                 0                 0                  0                 0                 1                 1                 0                 0   \n",
       "2                 0                 0                 0                 0                  0                 0                 1                 0                 1                 0   \n",
       "3                 0                 0                 0                 0                  0                 0                 1                 0                 1                 0   \n",
       "4                 0                 0                 0                 0                  0                 0                 1                 0                 1                 0   \n",
       "\n",
       "   BsmtFullBath_3.0  BsmtHalfBath_0.0  BsmtHalfBath_1.0  BsmtHalfBath_2.0  BsmtQual_Ex  BsmtQual_Fa  BsmtQual_Gd  BsmtQual_None  BsmtQual_TA  CentralAir_N  CentralAir_Y  Condition1_Artery  \\\n",
       "0                 0                 1                 0                 0            0            0            1              0            0             0             1                  0   \n",
       "1                 0                 0                 1                 0            0            0            1              0            0             0             1                  0   \n",
       "2                 0                 1                 0                 0            0            0            1              0            0             0             1                  0   \n",
       "3                 0                 1                 0                 0            0            0            0              0            1             0             1                  0   \n",
       "4                 0                 1                 0                 0            0            0            1              0            0             0             1                  0   \n",
       "\n",
       "   Condition1_Feedr  Condition1_Norm  Condition1_PosA  Condition1_PosN  Condition1_RRAe  Condition1_RRAn  Condition1_RRNe  Condition1_RRNn  Condition2_Artery  Condition2_Feedr  Condition2_Norm  \\\n",
       "0                 0                1                0                0                0                0                0                0                  0                 0                1   \n",
       "1                 1                0                0                0                0                0                0                0                  0                 0                1   \n",
       "2                 0                1                0                0                0                0                0                0                  0                 0                1   \n",
       "3                 0                1                0                0                0                0                0                0                  0                 0                1   \n",
       "4                 0                1                0                0                0                0                0                0                  0                 0                1   \n",
       "\n",
       "   Condition2_PosA  Condition2_PosN  Condition2_RRAe  Condition2_RRAn  Condition2_RRNn  Electrical_FuseA  Electrical_FuseF  Electrical_FuseP  Electrical_Mix  Electrical_SBrkr  ExterCond_Ex  \\\n",
       "0                0                0                0                0                0                 0                 0                 0               0                 1             0   \n",
       "1                0                0                0                0                0                 0                 0                 0               0                 1             0   \n",
       "2                0                0                0                0                0                 0                 0                 0               0                 1             0   \n",
       "3                0                0                0                0                0                 0                 0                 0               0                 1             0   \n",
       "4                0                0                0                0                0                 0                 0                 0               0                 1             0   \n",
       "\n",
       "   ExterCond_Fa  ExterCond_Gd  ExterCond_Po  ExterCond_TA  ExterQual_Ex  ExterQual_Fa  ExterQual_Gd  ExterQual_TA  Exterior1st_AsbShng  Exterior1st_AsphShn  Exterior1st_BrkComm  Exterior1st_BrkFace  \\\n",
       "0             0             0             0             1             0             0             1             0                    0                    0                    0                    0   \n",
       "1             0             0             0             1             0             0             0             1                    0                    0                    0                    0   \n",
       "2             0             0             0             1             0             0             1             0                    0                    0                    0                    0   \n",
       "3             0             0             0             1             0             0             0             1                    0                    0                    0                    0   \n",
       "4             0             0             0             1             0             0             1             0                    0                    0                    0                    0   \n",
       "\n",
       "   Exterior1st_CBlock  Exterior1st_CemntBd  Exterior1st_HdBoard  Exterior1st_ImStucc  Exterior1st_MetalSd  Exterior1st_Plywood  Exterior1st_Stone  Exterior1st_Stucco  Exterior1st_VinylSd  \\\n",
       "0                   0                    0                    0                    0                    0                    0                  0                   0                    1   \n",
       "1                   0                    0                    0                    0                    1                    0                  0                   0                    0   \n",
       "2                   0                    0                    0                    0                    0                    0                  0                   0                    1   \n",
       "3                   0                    0                    0                    0                    0                    0                  0                   0                    0   \n",
       "4                   0                    0                    0                    0                    0                    0                  0                   0                    1   \n",
       "\n",
       "   Exterior1st_Wd Sdng  Exterior1st_WdShing  Exterior2nd_AsbShng  Exterior2nd_AsphShn  Exterior2nd_Brk Cmn  Exterior2nd_BrkFace  Exterior2nd_CBlock  Exterior2nd_CmentBd  Exterior2nd_HdBoard  \\\n",
       "0                    0                    0                    0                    0                    0                    0                   0                    0                    0   \n",
       "1                    0                    0                    0                    0                    0                    0                   0                    0                    0   \n",
       "2                    0                    0                    0                    0                    0                    0                   0                    0                    0   \n",
       "3                    1                    0                    0                    0                    0                    0                   0                    0                    0   \n",
       "4                    0                    0                    0                    0                    0                    0                   0                    0                    0   \n",
       "\n",
       "   Exterior2nd_ImStucc  Exterior2nd_MetalSd  Exterior2nd_Other  Exterior2nd_Plywood  Exterior2nd_Stone  Exterior2nd_Stucco  Exterior2nd_VinylSd  Exterior2nd_Wd Sdng  Exterior2nd_Wd Shng  \\\n",
       "0                    0                    0                  0                    0                  0                   0                    1                    0                    0   \n",
       "1                    0                    1                  0                    0                  0                   0                    0                    0                    0   \n",
       "2                    0                    0                  0                    0                  0                   0                    1                    0                    0   \n",
       "3                    0                    0                  0                    0                  0                   0                    0                    0                    1   \n",
       "4                    0                    0                  0                    0                  0                   0                    1                    0                    0   \n",
       "\n",
       "   Fence_GdPrv  Fence_GdWo  Fence_MnPrv  Fence_MnWw  Fence_None  FireplaceQu_Ex  FireplaceQu_Fa  FireplaceQu_Gd  FireplaceQu_None  FireplaceQu_Po  FireplaceQu_TA  Foundation_BrkTil  \\\n",
       "0            0           0            0           0           1               0               0               0                 1               0               0                  0   \n",
       "1            0           0            0           0           1               0               0               0                 0               0               1                  0   \n",
       "2            0           0            0           0           1               0               0               0                 0               0               1                  0   \n",
       "3            0           0            0           0           1               0               0               1                 0               0               0                  1   \n",
       "4            0           0            0           0           1               0               0               0                 0               0               1                  0   \n",
       "\n",
       "   Foundation_CBlock  Foundation_PConc  Foundation_Slab  Foundation_Stone  Foundation_Wood  Functional_Maj1  Functional_Maj2  Functional_Min1  Functional_Min2  Functional_Mod  Functional_Sev  \\\n",
       "0                  0                 1                0                 0                0                0                0                0                0               0               0   \n",
       "1                  1                 0                0                 0                0                0                0                0                0               0               0   \n",
       "2                  0                 1                0                 0                0                0                0                0                0               0               0   \n",
       "3                  0                 0                0                 0                0                0                0                0                0               0               0   \n",
       "4                  0                 1                0                 0                0                0                0                0                0               0               0   \n",
       "\n",
       "   Functional_Typ  GarageCond_Ex  GarageCond_Fa  GarageCond_Gd  GarageCond_None  GarageCond_Po  GarageCond_TA  GarageFinish_Fin  GarageFinish_None  GarageFinish_RFn  GarageFinish_Unf  GarageQual_Ex  \\\n",
       "0               1              0              0              0                0              0              1                 0                  0                 1                 0              0   \n",
       "1               1              0              0              0                0              0              1                 0                  0                 1                 0              0   \n",
       "2               1              0              0              0                0              0              1                 0                  0                 1                 0              0   \n",
       "3               1              0              0              0                0              0              1                 0                  0                 0                 1              0   \n",
       "4               1              0              0              0                0              0              1                 0                  0                 1                 0              0   \n",
       "\n",
       "   GarageQual_Fa  GarageQual_Gd  GarageQual_None  GarageQual_Po  GarageQual_TA  GarageType_2Types  GarageType_Attchd  GarageType_Basment  GarageType_BuiltIn  GarageType_CarPort  GarageType_Detchd  \\\n",
       "0              0              0                0              0              1                  0                  1                   0                   0                   0                  0   \n",
       "1              0              0                0              0              1                  0                  1                   0                   0                   0                  0   \n",
       "2              0              0                0              0              1                  0                  1                   0                   0                   0                  0   \n",
       "3              0              0                0              0              1                  0                  0                   0                   0                   0                  1   \n",
       "4              0              0                0              0              1                  0                  1                   0                   0                   0                  0   \n",
       "\n",
       "   GarageType_None  HalfBath_0  HalfBath_1  HalfBath_2  Heating_Floor  Heating_GasA  Heating_GasW  Heating_Grav  Heating_OthW  Heating_Wall  HeatingQC_Ex  HeatingQC_Fa  HeatingQC_Gd  HeatingQC_Po  \\\n",
       "0                0           0           1           0              0             1             0             0             0             0             1             0             0             0   \n",
       "1                0           1           0           0              0             1             0             0             0             0             1             0             0             0   \n",
       "2                0           0           1           0              0             1             0             0             0             0             1             0             0             0   \n",
       "3                0           1           0           0              0             1             0             0             0             0             0             0             1             0   \n",
       "4                0           0           1           0              0             1             0             0             0             0             1             0             0             0   \n",
       "\n",
       "   HeatingQC_TA  HouseStyle_1.5Fin  HouseStyle_1.5Unf  HouseStyle_1Story  HouseStyle_2.5Fin  HouseStyle_2.5Unf  HouseStyle_2Story  HouseStyle_SFoyer  HouseStyle_SLvl  KitchenAbvGr_0  KitchenAbvGr_1  \\\n",
       "0             0                  0                  0                  0                  0                  0                  1                  0                0               0               1   \n",
       "1             0                  0                  0                  1                  0                  0                  0                  0                0               0               1   \n",
       "2             0                  0                  0                  0                  0                  0                  1                  0                0               0               1   \n",
       "3             0                  0                  0                  0                  0                  0                  1                  0                0               0               1   \n",
       "4             0                  0                  0                  0                  0                  0                  1                  0                0               0               1   \n",
       "\n",
       "   KitchenAbvGr_2  KitchenAbvGr_3  KitchenQual_Ex  KitchenQual_Fa  KitchenQual_Gd  KitchenQual_TA  LandContour_Bnk  LandContour_HLS  LandContour_Low  LandContour_Lvl  LandSlope_Gtl  LandSlope_Mod  \\\n",
       "0               0               0               0               0               1               0                0                0                0                1              1              0   \n",
       "1               0               0               0               0               0               1                0                0                0                1              1              0   \n",
       "2               0               0               0               0               1               0                0                0                0                1              1              0   \n",
       "3               0               0               0               0               1               0                0                0                0                1              1              0   \n",
       "4               0               0               0               0               1               0                0                0                0                1              1              0   \n",
       "\n",
       "   LandSlope_Sev  LotConfig_Corner  LotConfig_CulDSac  LotConfig_FR2  LotConfig_FR3  LotConfig_Inside  LotShape_IR1  LotShape_IR2  LotShape_IR3  LotShape_Reg  LowQualFinSF_0  LowQualFinSF_1064  \\\n",
       "0              0                 0                  0              0              0                 1             0             0             0             1               1                  0   \n",
       "1              0                 0                  0              1              0                 0             0             0             0             1               1                  0   \n",
       "2              0                 0                  0              0              0                 1             1             0             0             0               1                  0   \n",
       "3              0                 1                  0              0              0                 0             1             0             0             0               1                  0   \n",
       "4              0                 0                  0              1              0                 0             1             0             0             0               1                  0   \n",
       "\n",
       "   LowQualFinSF_108  LowQualFinSF_114  LowQualFinSF_120  LowQualFinSF_140  LowQualFinSF_144  LowQualFinSF_156  LowQualFinSF_205  LowQualFinSF_232  LowQualFinSF_234  LowQualFinSF_259  \\\n",
       "0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0   \n",
       "1                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0   \n",
       "2                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0   \n",
       "3                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0   \n",
       "4                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0   \n",
       "\n",
       "   LowQualFinSF_312  LowQualFinSF_360  LowQualFinSF_362  LowQualFinSF_371  LowQualFinSF_384  LowQualFinSF_390  LowQualFinSF_392  LowQualFinSF_397  LowQualFinSF_420  LowQualFinSF_431  \\\n",
       "0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0   \n",
       "1                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0   \n",
       "2                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0   \n",
       "3                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0   \n",
       "4                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0   \n",
       "\n",
       "   LowQualFinSF_436  LowQualFinSF_450  LowQualFinSF_473  LowQualFinSF_479  LowQualFinSF_481  LowQualFinSF_512  LowQualFinSF_513  LowQualFinSF_514  LowQualFinSF_515  LowQualFinSF_528  \\\n",
       "0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0   \n",
       "1                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0   \n",
       "2                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0   \n",
       "3                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0   \n",
       "4                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0   \n",
       "\n",
       "   LowQualFinSF_53  LowQualFinSF_572  LowQualFinSF_697  LowQualFinSF_80  MSSubClass_120  MSSubClass_150  MSSubClass_160  MSSubClass_180  MSSubClass_190  MSSubClass_20  MSSubClass_30  MSSubClass_40  \\\n",
       "0                0                 0                 0                0               0               0               0               0               0              0              0              0   \n",
       "1                0                 0                 0                0               0               0               0               0               0              1              0              0   \n",
       "2                0                 0                 0                0               0               0               0               0               0              0              0              0   \n",
       "3                0                 0                 0                0               0               0               0               0               0              0              0              0   \n",
       "4                0                 0                 0                0               0               0               0               0               0              0              0              0   \n",
       "\n",
       "   MSSubClass_45  MSSubClass_50  MSSubClass_60  MSSubClass_70  MSSubClass_75  MSSubClass_80  MSSubClass_85  MSSubClass_90  MSZoning_C (all)  MSZoning_FV  MSZoning_RH  MSZoning_RL  MSZoning_RM  \\\n",
       "0              0              0              1              0              0              0              0              0                 0            0            0            1            0   \n",
       "1              0              0              0              0              0              0              0              0                 0            0            0            1            0   \n",
       "2              0              0              1              0              0              0              0              0                 0            0            0            1            0   \n",
       "3              0              0              0              1              0              0              0              0                 0            0            0            1            0   \n",
       "4              0              0              1              0              0              0              0              0                 0            0            0            1            0   \n",
       "\n",
       "   MasVnrType_BrkCmn  MasVnrType_BrkFace  MasVnrType_None  MasVnrType_Stone  MiscFeature_Gar2  MiscFeature_None  MiscFeature_Othr  MiscFeature_Shed  MiscFeature_TenC  MoSold_1  MoSold_10  MoSold_11  \\\n",
       "0                  0                   1                0                 0                 0                 1                 0                 0                 0         0          0          0   \n",
       "1                  0                   0                1                 0                 0                 1                 0                 0                 0         0          0          0   \n",
       "2                  0                   1                0                 0                 0                 1                 0                 0                 0         0          0          0   \n",
       "3                  0                   0                1                 0                 0                 1                 0                 0                 0         0          0          0   \n",
       "4                  0                   1                0                 0                 0                 1                 0                 0                 0         0          0          0   \n",
       "\n",
       "   MoSold_12  MoSold_2  MoSold_3  MoSold_4  MoSold_5  MoSold_6  MoSold_7  MoSold_8  MoSold_9  Neighborhood_Blmngtn  Neighborhood_Blueste  Neighborhood_BrDale  Neighborhood_BrkSide  \\\n",
       "0          0         1         0         0         0         0         0         0         0                     0                     0                    0                     0   \n",
       "1          0         0         0         0         1         0         0         0         0                     0                     0                    0                     0   \n",
       "2          0         0         0         0         0         0         0         0         1                     0                     0                    0                     0   \n",
       "3          0         1         0         0         0         0         0         0         0                     0                     0                    0                     0   \n",
       "4          1         0         0         0         0         0         0         0         0                     0                     0                    0                     0   \n",
       "\n",
       "   Neighborhood_ClearCr  Neighborhood_CollgCr  Neighborhood_Crawfor  Neighborhood_Edwards  Neighborhood_Gilbert  Neighborhood_IDOTRR  Neighborhood_MeadowV  Neighborhood_Mitchel  Neighborhood_NAmes  \\\n",
       "0                     0                     1                     0                     0                     0                    0                     0                     0                   0   \n",
       "1                     0                     0                     0                     0                     0                    0                     0                     0                   0   \n",
       "2                     0                     1                     0                     0                     0                    0                     0                     0                   0   \n",
       "3                     0                     0                     1                     0                     0                    0                     0                     0                   0   \n",
       "4                     0                     0                     0                     0                     0                    0                     0                     0                   0   \n",
       "\n",
       "   Neighborhood_NPkVill  Neighborhood_NWAmes  Neighborhood_NoRidge  Neighborhood_NridgHt  Neighborhood_OldTown  Neighborhood_SWISU  Neighborhood_Sawyer  Neighborhood_SawyerW  Neighborhood_Somerst  \\\n",
       "0                     0                    0                     0                     0                     0                   0                    0                     0                     0   \n",
       "1                     0                    0                     0                     0                     0                   0                    0                     0                     0   \n",
       "2                     0                    0                     0                     0                     0                   0                    0                     0                     0   \n",
       "3                     0                    0                     0                     0                     0                   0                    0                     0                     0   \n",
       "4                     0                    0                     1                     0                     0                   0                    0                     0                     0   \n",
       "\n",
       "   Neighborhood_StoneBr  Neighborhood_Timber  Neighborhood_Veenker  PavedDrive_N  PavedDrive_P  PavedDrive_Y  PoolQC_Ex  PoolQC_Fa  PoolQC_Gd  PoolQC_None  RoofMatl_CompShg  RoofMatl_Membran  \\\n",
       "0                     0                    0                     0             0             0             1          0          0          0            1                 1                 0   \n",
       "1                     0                    0                     1             0             0             1          0          0          0            1                 1                 0   \n",
       "2                     0                    0                     0             0             0             1          0          0          0            1                 1                 0   \n",
       "3                     0                    0                     0             0             0             1          0          0          0            1                 1                 0   \n",
       "4                     0                    0                     0             0             0             1          0          0          0            1                 1                 0   \n",
       "\n",
       "   RoofMatl_Metal  RoofMatl_Roll  RoofMatl_Tar&Grv  RoofMatl_WdShake  RoofMatl_WdShngl  RoofStyle_Flat  RoofStyle_Gable  RoofStyle_Gambrel  RoofStyle_Hip  RoofStyle_Mansard  RoofStyle_Shed  \\\n",
       "0               0              0                 0                 0                 0               0                1                  0              0                  0               0   \n",
       "1               0              0                 0                 0                 0               0                1                  0              0                  0               0   \n",
       "2               0              0                 0                 0                 0               0                1                  0              0                  0               0   \n",
       "3               0              0                 0                 0                 0               0                1                  0              0                  0               0   \n",
       "4               0              0                 0                 0                 0               0                1                  0              0                  0               0   \n",
       "\n",
       "   SaleCondition_Abnorml  SaleCondition_AdjLand  SaleCondition_Alloca  SaleCondition_Family  SaleCondition_Normal  SaleCondition_Partial  SaleType_COD  SaleType_CWD  SaleType_Con  SaleType_ConLD  \\\n",
       "0                      0                      0                     0                     0                     1                      0             0             0             0               0   \n",
       "1                      0                      0                     0                     0                     1                      0             0             0             0               0   \n",
       "2                      0                      0                     0                     0                     1                      0             0             0             0               0   \n",
       "3                      1                      0                     0                     0                     0                      0             0             0             0               0   \n",
       "4                      0                      0                     0                     0                     1                      0             0             0             0               0   \n",
       "\n",
       "   SaleType_ConLI  SaleType_ConLw  SaleType_New  SaleType_Oth  SaleType_WD  Street_Grvl  Street_Pave  Utilities_AllPub  Utilities_NoSeWa  YrSold_2006  YrSold_2007  YrSold_2008  YrSold_2009  \\\n",
       "0               0               0             0             0            1            0            1                 1                 0            0            0            1            0   \n",
       "1               0               0             0             0            1            0            1                 1                 0            0            1            0            0   \n",
       "2               0               0             0             0            1            0            1                 1                 0            0            0            1            0   \n",
       "3               0               0             0             0            1            0            1                 1                 0            1            0            0            0   \n",
       "4               0               0             0             0            1            0            1                 1                 0            0            0            1            0   \n",
       "\n",
       "   YrSold_2010  \n",
       "0            0  \n",
       "1            0  \n",
       "2            0  \n",
       "3            0  \n",
       "4            0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pipe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ __use robustscaler since maybe there are other outliers.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = RobustScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_train=train.shape[0]\n",
    "\n",
    "X = data_pipe[:n_train]\n",
    "test_X = data_pipe[n_train:]\n",
    "y= train.SalePrice\n",
    "\n",
    "X_scaled = scaler.fit(X).transform(X)\n",
    "y_log = np.log(train.SalePrice)\n",
    "test_X_scaled = scaler.transform(test_X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ __I have to confess, the feature engineering above is not enough, so we need more.__   \n",
    "+ __Combining different features is usually a good way, but we have no idea what features should we choose. Luckily there are some models that can provide feature selection, here I use Lasso, but you are free to choose Ridge, RandomForest or GradientBoostingTree.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=0.001, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "   normalize=False, positive=False, precompute=False, random_state=None,\n",
       "   selection='cyclic', tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso=Lasso(alpha=0.001)\n",
    "lasso.fit(X_scaled,y_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "FI_lasso = pd.DataFrame({\"Feature Importance\":lasso.coef_}, index=data_pipe.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GrLivArea</th>\n",
       "      <td>1.088701e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallQual</th>\n",
       "      <td>1.025828e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <td>7.422145e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearBuilt</th>\n",
       "      <td>6.892575e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1stFlrSF</th>\n",
       "      <td>5.976035e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Functional_Typ</th>\n",
       "      <td>5.186168e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <td>5.026054e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_Crawfor</th>\n",
       "      <td>4.994223e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallCond</th>\n",
       "      <td>4.552726e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oNeighborhood</th>\n",
       "      <td>4.431058e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st_BrkFace</th>\n",
       "      <td>4.377180e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oSaleCondition</th>\n",
       "      <td>4.164035e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <td>4.066318e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtQual_Ex</th>\n",
       "      <td>4.035629e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oMSZoning</th>\n",
       "      <td>4.004052e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenQual_Ex</th>\n",
       "      <td>3.957155e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_BrkSide</th>\n",
       "      <td>3.809928e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtExposure_Gd</th>\n",
       "      <td>3.344413e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition1_Norm</th>\n",
       "      <td>3.331946e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotArea</th>\n",
       "      <td>2.865886e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCars</th>\n",
       "      <td>2.468419e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HalfBath_1</th>\n",
       "      <td>2.027449e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Foundation_PConc</th>\n",
       "      <td>1.780704e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <td>1.752597e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageArea</th>\n",
       "      <td>1.745431e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <td>1.681616e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fireplaces</th>\n",
       "      <td>1.681003e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotConfig_CulDSac</th>\n",
       "      <td>1.357443e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oHeatingQC</th>\n",
       "      <td>1.276122e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oMSSubClass</th>\n",
       "      <td>1.205278e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoolArea</th>\n",
       "      <td>1.186408e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oKitchenQual</th>\n",
       "      <td>1.099964e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PavedDrive_Y</th>\n",
       "      <td>1.073762e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oBsmtExposure</th>\n",
       "      <td>1.021362e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HeatingQC_Ex</th>\n",
       "      <td>1.017304e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExterCond_TA</th>\n",
       "      <td>9.443560e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oExterior1st</th>\n",
       "      <td>8.764696e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrType_Stone</th>\n",
       "      <td>8.493649e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_StoneBr</th>\n",
       "      <td>8.404839e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MoSold_5</th>\n",
       "      <td>7.446006e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCond_TA</th>\n",
       "      <td>7.413880e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st_MetalSd</th>\n",
       "      <td>7.310067e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullBath</th>\n",
       "      <td>7.246272e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ScreenPorch</th>\n",
       "      <td>6.927560e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oExterQual</th>\n",
       "      <td>6.637892e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oGarageFinish</th>\n",
       "      <td>5.958674e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oFireplaceQu</th>\n",
       "      <td>5.714626e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oCondition1</th>\n",
       "      <td>4.244992e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BedroomAbvGr_2</th>\n",
       "      <td>3.692826e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FireplaceQu_Gd</th>\n",
       "      <td>3.527221e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MoSold_6</th>\n",
       "      <td>2.628544e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <td>2.440622e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <td>2.308675e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3SsnPorch</th>\n",
       "      <td>2.075395e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MoSold_7</th>\n",
       "      <td>2.071031e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageType_Detchd</th>\n",
       "      <td>1.273244e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <td>1.225513e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oBsmtQual</th>\n",
       "      <td>8.765618e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass_20</th>\n",
       "      <td>5.745895e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oMasVnrType</th>\n",
       "      <td>4.261606e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YrSold_2006</th>\n",
       "      <td>3.445679e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1_GLQ</th>\n",
       "      <td>3.428838e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CentralAir_Y</th>\n",
       "      <td>1.370121e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_512</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass_50</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass_45</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass_40</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_384</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_390</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass_190</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_392</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass_180</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_397</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_420</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass_160</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_431</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass_150</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_513</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_436</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass_120</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_80</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_362</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_697</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_572</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_53</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_528</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_450</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_473</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_479</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_515</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_514</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_481</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_371</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotShape_Reg</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_360</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenAbvGr_1</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LandContour_Lvl</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LandContour_Low</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LandContour_HLS</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LandContour_Bnk</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenQual_TA</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenQual_Gd</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenQual_Fa</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenAbvGr_3</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenAbvGr_0</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_312</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HouseStyle_SLvl</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HouseStyle_SFoyer</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HouseStyle_2Story</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HouseStyle_2.5Unf</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HouseStyle_1.5Fin</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HouseStyle_2.5Fin</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HouseStyle_1Story</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HouseStyle_1.5Unf</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LandSlope_Gtl</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LandSlope_Mod</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LandSlope_Sev</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotConfig_Corner</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_259</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_234</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_232</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_205</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_156</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_144</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_140</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_120</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_114</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_108</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_1064</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF_0</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotShape_IR3</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotShape_IR2</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass_60</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotConfig_FR3</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotConfig_FR2</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotShape_IR1</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MoSold_3</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass_70</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofMatl_Membran</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofStyle_Gambrel</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofStyle_Gable</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofStyle_Flat</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofMatl_WdShngl</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofMatl_WdShake</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofMatl_Tar&amp;Grv</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofMatl_Roll</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofMatl_Metal</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofMatl_CompShg</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofStyle_Mansard</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoolQC_None</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoolQC_Gd</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoolQC_Fa</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoolQC_Ex</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PavedDrive_P</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PavedDrive_N</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_Veenker</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_Timber</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofStyle_Hip</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofStyle_Shed</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass_75</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleType_ConLw</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YrSold_2008</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YrSold_2007</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utilities_NoSeWa</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utilities_AllPub</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Street_Pave</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Street_Grvl</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleType_New</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleType_ConLI</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleType_ConLD</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleType_Con</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleType_CWD</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleType_COD</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleCondition_Partial</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleCondition_Normal</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleCondition_Family</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleCondition_Alloca</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_Somerst</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_SawyerW</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_Sawyer</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrType_None</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MoSold_11</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MoSold_10</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MoSold_1</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscFeature_TenC</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscFeature_Shed</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscFeature_Othr</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscFeature_None</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscFeature_Gar2</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrType_BrkFace</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_SWISU</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrType_BrkCmn</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSZoning_RM</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSZoning_RL</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSZoning_RH</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSZoning_FV</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass_90</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass_85</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass_80</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MoSold_12</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MoSold_2</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HeatingQC_Po</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MoSold_4</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_OldTown</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_NridgHt</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_NoRidge</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_NPkVill</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_NAmes</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_Mitchel</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_MeadowV</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_IDOTRR</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_Gilbert</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_Edwards</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_CollgCr</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_ClearCr</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_BrDale</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_Blueste</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_Blmngtn</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MoSold_9</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MoSold_8</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HeatingQC_TA</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageQual_None</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HeatingQC_Gd</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtQual_Fa</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition1_RRAe</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition1_PosN</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition1_PosA</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition1_Feedr</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition1_Artery</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtQual_TA</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtQual_None</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtQual_Gd</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtHalfBath_2.0</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExterQual_Gd</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtHalfBath_1.0</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtHalfBath_0.0</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFullBath_3.0</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFullBath_2.0</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFullBath_1.0</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType2_Unf</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType2_Rec</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType2_None</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition1_RRAn</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition1_RRNe</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition1_RRNn</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition2_Artery</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExterQual_Ex</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExterCond_Po</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExterCond_Gd</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HeatingQC_Fa</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExterCond_Ex</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electrical_SBrkr</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electrical_Mix</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electrical_FuseP</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electrical_FuseF</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electrical_FuseA</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition2_RRNn</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition2_RRAn</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition2_RRAe</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition2_PosN</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition2_PosA</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition2_Norm</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition2_Feedr</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType2_LwQ</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType2_GLQ</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType2_BLQ</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BedroomAbvGr_5</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BedroomAbvGr_3</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BedroomAbvGr_1</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BedroomAbvGr_0</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alley_Pave</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alley_None</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alley_Grvl</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oSaleType</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oPavedDrive</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oGarageType</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oFunctional</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oHeating</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oFoundation</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oBldgType</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BedroomAbvGr_4</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BedroomAbvGr_6</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType2_ALQ</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BedroomAbvGr_8</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1_Rec</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1_None</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1_LwQ</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1_BLQ</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1_ALQ</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtExposure_None</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtExposure_Mn</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtExposure_Av</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtCond_TA</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtCond_Po</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtCond_None</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtCond_Gd</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BldgType_TwnhsE</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BldgType_Twnhs</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BldgType_Duplex</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BldgType_2fmCon</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BldgType_1Fam</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExterQual_Fa</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExterCond_Fa</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExterQual_TA</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Functional_Mod</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageFinish_None</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageFinish_Fin</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCond_Po</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCond_None</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCond_Gd</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCond_Fa</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCond_Ex</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Functional_Sev</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Functional_Min2</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FireplaceQu_None</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Functional_Min1</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Functional_Maj2</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Functional_Maj1</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Foundation_Wood</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Foundation_Stone</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Foundation_Slab</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Foundation_CBlock</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FireplaceQu_TA</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageFinish_RFn</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageFinish_Unf</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageQual_Ex</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageQual_Fa</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heating_Wall</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heating_OthW</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heating_Grav</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heating_GasW</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heating_GasA</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heating_Floor</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HalfBath_2</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HalfBath_0</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageType_None</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageType_CarPort</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageType_BuiltIn</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageType_Basment</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageType_Attchd</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageType_2Types</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageQual_TA</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageQual_Po</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageQual_Gd</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FireplaceQu_Po</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YrSold_2010</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd_CBlock</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd_AsbShng</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st_CBlock</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd_HdBoard</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd_CmentBd</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FireplaceQu_Fa</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd_BrkFace</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd_Brk Cmn</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st_AsphShn</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd_AsphShn</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st_WdShing</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd_Other</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st_VinylSd</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st_Stucco</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st_BrkComm</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st_Stone</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st_Plywood</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st_ImStucc</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st_HdBoard</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st_CemntBd</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd_MetalSd</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd_ImStucc</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd_Plywood</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd_Wd Shng</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FireplaceQu_Ex</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st_AsbShng</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fence_None</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fence_MnWw</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fence_MnPrv</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fence_GdPrv</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fence_GdWo</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd_Wd Sdng</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd_VinylSd</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd_Stucco</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd_Stone</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtExposure_No</th>\n",
       "      <td>-1.789030e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtCond_Fa</th>\n",
       "      <td>-6.245991e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass_30</th>\n",
       "      <td>-8.287407e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st_Wd Sdng</th>\n",
       "      <td>-1.045455e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotConfig_Inside</th>\n",
       "      <td>-1.387029e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscVal</th>\n",
       "      <td>-2.443289e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <td>-3.412652e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleType_WD</th>\n",
       "      <td>-3.986946e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Foundation_BrkTil</th>\n",
       "      <td>-5.401002e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oHouseStyle</th>\n",
       "      <td>-6.810118e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YrSold_2009</th>\n",
       "      <td>-8.841903e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood_NWAmes</th>\n",
       "      <td>-1.000781e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFullBath_0.0</th>\n",
       "      <td>-1.618491e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1_Unf</th>\n",
       "      <td>-1.669639e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleCondition_Abnorml</th>\n",
       "      <td>-1.883348e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenAbvGr_2</th>\n",
       "      <td>-2.707554e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CentralAir_N</th>\n",
       "      <td>-3.403050e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSZoning_C (all)</th>\n",
       "      <td>-9.682124e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Feature Importance\n",
       "GrLivArea                    1.088701e-01\n",
       "OverallQual                  1.025828e-01\n",
       "2ndFlrSF                     7.422145e-02\n",
       "YearBuilt                    6.892575e-02\n",
       "1stFlrSF                     5.976035e-02\n",
       "Functional_Typ               5.186168e-02\n",
       "TotalBsmtSF                  5.026054e-02\n",
       "Neighborhood_Crawfor         4.994223e-02\n",
       "OverallCond                  4.552726e-02\n",
       "oNeighborhood                4.431058e-02\n",
       "Exterior1st_BrkFace          4.377180e-02\n",
       "oSaleCondition               4.164035e-02\n",
       "BsmtFinSF1                   4.066318e-02\n",
       "BsmtQual_Ex                  4.035629e-02\n",
       "oMSZoning                    4.004052e-02\n",
       "KitchenQual_Ex               3.957155e-02\n",
       "Neighborhood_BrkSide         3.809928e-02\n",
       "BsmtExposure_Gd              3.344413e-02\n",
       "Condition1_Norm              3.331946e-02\n",
       "LotArea                      2.865886e-02\n",
       "GarageCars                   2.468419e-02\n",
       "HalfBath_1                   2.027449e-02\n",
       "Foundation_PConc             1.780704e-02\n",
       "YearRemodAdd                 1.752597e-02\n",
       "GarageArea                   1.745431e-02\n",
       "WoodDeckSF                   1.681616e-02\n",
       "Fireplaces                   1.681003e-02\n",
       "LotConfig_CulDSac            1.357443e-02\n",
       "oHeatingQC                   1.276122e-02\n",
       "oMSSubClass                  1.205278e-02\n",
       "PoolArea                     1.186408e-02\n",
       "oKitchenQual                 1.099964e-02\n",
       "PavedDrive_Y                 1.073762e-02\n",
       "oBsmtExposure                1.021362e-02\n",
       "HeatingQC_Ex                 1.017304e-02\n",
       "ExterCond_TA                 9.443560e-03\n",
       "oExterior1st                 8.764696e-03\n",
       "MasVnrType_Stone             8.493649e-03\n",
       "Neighborhood_StoneBr         8.404839e-03\n",
       "MoSold_5                     7.446006e-03\n",
       "GarageCond_TA                7.413880e-03\n",
       "Exterior1st_MetalSd          7.310067e-03\n",
       "FullBath                     7.246272e-03\n",
       "ScreenPorch                  6.927560e-03\n",
       "oExterQual                   6.637892e-03\n",
       "oGarageFinish                5.958674e-03\n",
       "oFireplaceQu                 5.714626e-03\n",
       "oCondition1                  4.244992e-03\n",
       "BedroomAbvGr_2               3.692826e-03\n",
       "FireplaceQu_Gd               3.527221e-03\n",
       "MoSold_6                     2.628544e-03\n",
       "EnclosedPorch                2.440622e-03\n",
       "TotRmsAbvGrd                 2.308675e-03\n",
       "3SsnPorch                    2.075395e-03\n",
       "MoSold_7                     2.071031e-03\n",
       "GarageType_Detchd            1.273244e-03\n",
       "OpenPorchSF                  1.225513e-03\n",
       "oBsmtQual                    8.765618e-04\n",
       "MSSubClass_20                5.745895e-04\n",
       "oMasVnrType                  4.261606e-04\n",
       "YrSold_2006                  3.445679e-04\n",
       "BsmtFinType1_GLQ             3.428838e-04\n",
       "CentralAir_Y                 1.370121e-15\n",
       "LowQualFinSF_512             0.000000e+00\n",
       "MSSubClass_50               -0.000000e+00\n",
       "MSSubClass_45                0.000000e+00\n",
       "MSSubClass_40               -0.000000e+00\n",
       "LowQualFinSF_384            -0.000000e+00\n",
       "LowQualFinSF_390             0.000000e+00\n",
       "MSSubClass_190              -0.000000e+00\n",
       "LowQualFinSF_392            -0.000000e+00\n",
       "MSSubClass_180               0.000000e+00\n",
       "LowQualFinSF_397            -0.000000e+00\n",
       "LowQualFinSF_420            -0.000000e+00\n",
       "MSSubClass_160              -0.000000e+00\n",
       "LowQualFinSF_431             0.000000e+00\n",
       "MSSubClass_150               0.000000e+00\n",
       "LowQualFinSF_513             0.000000e+00\n",
       "LowQualFinSF_436             0.000000e+00\n",
       "MSSubClass_120               0.000000e+00\n",
       "LowQualFinSF_80             -0.000000e+00\n",
       "LowQualFinSF_362             0.000000e+00\n",
       "LowQualFinSF_697             0.000000e+00\n",
       "LowQualFinSF_572             0.000000e+00\n",
       "LowQualFinSF_53              0.000000e+00\n",
       "LowQualFinSF_528             0.000000e+00\n",
       "LowQualFinSF_450             0.000000e+00\n",
       "LowQualFinSF_473            -0.000000e+00\n",
       "LowQualFinSF_479            -0.000000e+00\n",
       "LowQualFinSF_515             0.000000e+00\n",
       "LowQualFinSF_514            -0.000000e+00\n",
       "LowQualFinSF_481            -0.000000e+00\n",
       "LowQualFinSF_371            -0.000000e+00\n",
       "LotShape_Reg                -0.000000e+00\n",
       "LowQualFinSF_360             0.000000e+00\n",
       "KitchenAbvGr_1               0.000000e+00\n",
       "LandContour_Lvl             -0.000000e+00\n",
       "LandContour_Low             -0.000000e+00\n",
       "LandContour_HLS              0.000000e+00\n",
       "LandContour_Bnk              0.000000e+00\n",
       "KitchenQual_TA              -0.000000e+00\n",
       "KitchenQual_Gd              -0.000000e+00\n",
       "KitchenQual_Fa               0.000000e+00\n",
       "KitchenAbvGr_3              -0.000000e+00\n",
       "KitchenAbvGr_0               0.000000e+00\n",
       "LowQualFinSF_312             0.000000e+00\n",
       "HouseStyle_SLvl              0.000000e+00\n",
       "HouseStyle_SFoyer            0.000000e+00\n",
       "HouseStyle_2Story           -0.000000e+00\n",
       "HouseStyle_2.5Unf            0.000000e+00\n",
       "HouseStyle_1.5Fin           -0.000000e+00\n",
       "HouseStyle_2.5Fin            0.000000e+00\n",
       "HouseStyle_1Story           -0.000000e+00\n",
       "HouseStyle_1.5Unf            0.000000e+00\n",
       "LandSlope_Gtl               -0.000000e+00\n",
       "LandSlope_Mod                0.000000e+00\n",
       "LandSlope_Sev               -0.000000e+00\n",
       "LotConfig_Corner             0.000000e+00\n",
       "LowQualFinSF_259             0.000000e+00\n",
       "LowQualFinSF_234            -0.000000e+00\n",
       "LowQualFinSF_232            -0.000000e+00\n",
       "LowQualFinSF_205            -0.000000e+00\n",
       "LowQualFinSF_156            -0.000000e+00\n",
       "LowQualFinSF_144             0.000000e+00\n",
       "LowQualFinSF_140             0.000000e+00\n",
       "LowQualFinSF_120            -0.000000e+00\n",
       "LowQualFinSF_114             0.000000e+00\n",
       "LowQualFinSF_108             0.000000e+00\n",
       "LowQualFinSF_1064            0.000000e+00\n",
       "LowQualFinSF_0               0.000000e+00\n",
       "LotShape_IR3                 0.000000e+00\n",
       "LotShape_IR2                 0.000000e+00\n",
       "MSSubClass_60               -0.000000e+00\n",
       "LotConfig_FR3               -0.000000e+00\n",
       "LotConfig_FR2               -0.000000e+00\n",
       "LotShape_IR1                -0.000000e+00\n",
       "MoSold_3                    -0.000000e+00\n",
       "MSSubClass_70                0.000000e+00\n",
       "RoofMatl_Membran             0.000000e+00\n",
       "RoofStyle_Gambrel           -0.000000e+00\n",
       "RoofStyle_Gable             -0.000000e+00\n",
       "RoofStyle_Flat               0.000000e+00\n",
       "RoofMatl_WdShngl             0.000000e+00\n",
       "RoofMatl_WdShake             0.000000e+00\n",
       "RoofMatl_Tar&Grv            -0.000000e+00\n",
       "RoofMatl_Roll               -0.000000e+00\n",
       "RoofMatl_Metal               0.000000e+00\n",
       "RoofMatl_CompShg            -0.000000e+00\n",
       "RoofStyle_Mansard            0.000000e+00\n",
       "PoolQC_None                 -0.000000e+00\n",
       "PoolQC_Gd                    0.000000e+00\n",
       "PoolQC_Fa                   -0.000000e+00\n",
       "PoolQC_Ex                    0.000000e+00\n",
       "PavedDrive_P                -0.000000e+00\n",
       "PavedDrive_N                -0.000000e+00\n",
       "Neighborhood_Veenker        -0.000000e+00\n",
       "Neighborhood_Timber         -0.000000e+00\n",
       "RoofStyle_Hip                0.000000e+00\n",
       "RoofStyle_Shed               0.000000e+00\n",
       "MSSubClass_75                0.000000e+00\n",
       "SaleType_ConLw              -0.000000e+00\n",
       "YrSold_2008                  0.000000e+00\n",
       "YrSold_2007                  0.000000e+00\n",
       "Utilities_NoSeWa            -0.000000e+00\n",
       "Utilities_AllPub             0.000000e+00\n",
       "Street_Pave                  0.000000e+00\n",
       "Street_Grvl                 -0.000000e+00\n",
       "SaleType_Oth                 0.000000e+00\n",
       "SaleType_New                 0.000000e+00\n",
       "SaleType_ConLI              -0.000000e+00\n",
       "SaleCondition_AdjLand        0.000000e+00\n",
       "SaleType_ConLD               0.000000e+00\n",
       "SaleType_Con                 0.000000e+00\n",
       "SaleType_CWD                 0.000000e+00\n",
       "SaleType_COD                -0.000000e+00\n",
       "SaleCondition_Partial        0.000000e+00\n",
       "SaleCondition_Normal         0.000000e+00\n",
       "SaleCondition_Family        -0.000000e+00\n",
       "SaleCondition_Alloca         0.000000e+00\n",
       "Neighborhood_Somerst         0.000000e+00\n",
       "Neighborhood_SawyerW        -0.000000e+00\n",
       "Neighborhood_Sawyer          0.000000e+00\n",
       "MasVnrType_None              0.000000e+00\n",
       "MoSold_11                   -0.000000e+00\n",
       "MoSold_10                    0.000000e+00\n",
       "MoSold_1                    -0.000000e+00\n",
       "MiscFeature_TenC            -0.000000e+00\n",
       "MiscFeature_Shed             0.000000e+00\n",
       "MiscFeature_Othr            -0.000000e+00\n",
       "MiscFeature_None             0.000000e+00\n",
       "MiscFeature_Gar2             0.000000e+00\n",
       "MasVnrType_BrkFace          -0.000000e+00\n",
       "Neighborhood_SWISU           0.000000e+00\n",
       "MasVnrType_BrkCmn           -0.000000e+00\n",
       "MSZoning_RM                  0.000000e+00\n",
       "MSZoning_RL                 -0.000000e+00\n",
       "MSZoning_RH                  0.000000e+00\n",
       "MSZoning_FV                  0.000000e+00\n",
       "MSSubClass_90               -0.000000e+00\n",
       "MSSubClass_85                0.000000e+00\n",
       "MSSubClass_80                0.000000e+00\n",
       "MoSold_12                    0.000000e+00\n",
       "MoSold_2                    -0.000000e+00\n",
       "HeatingQC_Po                -0.000000e+00\n",
       "MoSold_4                    -0.000000e+00\n",
       "Neighborhood_OldTown        -0.000000e+00\n",
       "Neighborhood_NridgHt         0.000000e+00\n",
       "Neighborhood_NoRidge         0.000000e+00\n",
       "Neighborhood_NPkVill         0.000000e+00\n",
       "Neighborhood_NAmes           0.000000e+00\n",
       "Neighborhood_Mitchel        -0.000000e+00\n",
       "Neighborhood_MeadowV        -0.000000e+00\n",
       "Neighborhood_IDOTRR         -0.000000e+00\n",
       "Neighborhood_Gilbert         0.000000e+00\n",
       "Neighborhood_Edwards        -0.000000e+00\n",
       "Neighborhood_CollgCr        -0.000000e+00\n",
       "Neighborhood_ClearCr         0.000000e+00\n",
       "Neighborhood_BrDale          0.000000e+00\n",
       "Neighborhood_Blueste         0.000000e+00\n",
       "Neighborhood_Blmngtn        -0.000000e+00\n",
       "MoSold_9                    -0.000000e+00\n",
       "MoSold_8                    -0.000000e+00\n",
       "HeatingQC_TA                -0.000000e+00\n",
       "GarageQual_None             -0.000000e+00\n",
       "HeatingQC_Gd                -0.000000e+00\n",
       "BsmtQual_Fa                 -0.000000e+00\n",
       "Condition1_RRAe             -0.000000e+00\n",
       "Condition1_PosN              0.000000e+00\n",
       "Condition1_PosA             -0.000000e+00\n",
       "Condition1_Feedr            -0.000000e+00\n",
       "Condition1_Artery           -0.000000e+00\n",
       "BsmtQual_TA                 -0.000000e+00\n",
       "BsmtQual_None                0.000000e+00\n",
       "BsmtQual_Gd                 -0.000000e+00\n",
       "BsmtHalfBath_2.0             0.000000e+00\n",
       "ExterQual_Gd                -0.000000e+00\n",
       "BsmtHalfBath_1.0            -0.000000e+00\n",
       "BsmtHalfBath_0.0             0.000000e+00\n",
       "BsmtFullBath_3.0             0.000000e+00\n",
       "BsmtFullBath_2.0             0.000000e+00\n",
       "BsmtFullBath_1.0             0.000000e+00\n",
       "BsmtFinType2_Unf            -0.000000e+00\n",
       "BsmtFinType2_Rec            -0.000000e+00\n",
       "BsmtFinType2_None            0.000000e+00\n",
       "Condition1_RRAn              0.000000e+00\n",
       "Condition1_RRNe             -0.000000e+00\n",
       "Condition1_RRNn              0.000000e+00\n",
       "Condition2_Artery           -0.000000e+00\n",
       "ExterQual_Ex                 0.000000e+00\n",
       "ExterCond_Po                -0.000000e+00\n",
       "ExterCond_Gd                -0.000000e+00\n",
       "HeatingQC_Fa                -0.000000e+00\n",
       "ExterCond_Ex                 0.000000e+00\n",
       "Electrical_SBrkr            -0.000000e+00\n",
       "Electrical_Mix              -0.000000e+00\n",
       "Electrical_FuseP            -0.000000e+00\n",
       "Electrical_FuseF             0.000000e+00\n",
       "Electrical_FuseA             0.000000e+00\n",
       "Condition2_RRNn              0.000000e+00\n",
       "Condition2_RRAn             -0.000000e+00\n",
       "Condition2_RRAe             -0.000000e+00\n",
       "Condition2_PosN             -0.000000e+00\n",
       "Condition2_PosA              0.000000e+00\n",
       "Condition2_Norm              0.000000e+00\n",
       "Condition2_Feedr             0.000000e+00\n",
       "BsmtFinType2_LwQ            -0.000000e+00\n",
       "BsmtFinType2_GLQ             0.000000e+00\n",
       "BsmtFinType2_BLQ            -0.000000e+00\n",
       "BedroomAbvGr_5              -0.000000e+00\n",
       "BedroomAbvGr_3              -0.000000e+00\n",
       "BedroomAbvGr_1              -0.000000e+00\n",
       "BedroomAbvGr_0               0.000000e+00\n",
       "Alley_Pave                   0.000000e+00\n",
       "Alley_None                  -0.000000e+00\n",
       "Alley_Grvl                  -0.000000e+00\n",
       "oSaleType                    0.000000e+00\n",
       "oPavedDrive                  0.000000e+00\n",
       "oGarageType                  0.000000e+00\n",
       "oFunctional                  0.000000e+00\n",
       "oHeating                     0.000000e+00\n",
       "oFoundation                  0.000000e+00\n",
       "oBldgType                    0.000000e+00\n",
       "MasVnrArea                   0.000000e+00\n",
       "LotFrontage                  0.000000e+00\n",
       "GarageYrBlt                  0.000000e+00\n",
       "BsmtFinSF2                  -0.000000e+00\n",
       "BedroomAbvGr_4               0.000000e+00\n",
       "BedroomAbvGr_6              -0.000000e+00\n",
       "BsmtFinType2_ALQ             0.000000e+00\n",
       "BedroomAbvGr_8               0.000000e+00\n",
       "BsmtFinType1_Rec            -0.000000e+00\n",
       "BsmtFinType1_None            0.000000e+00\n",
       "BsmtFinType1_LwQ            -0.000000e+00\n",
       "BsmtFinType1_BLQ             0.000000e+00\n",
       "BsmtFinType1_ALQ             0.000000e+00\n",
       "BsmtExposure_None            0.000000e+00\n",
       "BsmtExposure_Mn             -0.000000e+00\n",
       "BsmtExposure_Av             -0.000000e+00\n",
       "BsmtCond_TA                  0.000000e+00\n",
       "BsmtCond_Po                 -0.000000e+00\n",
       "BsmtCond_None                0.000000e+00\n",
       "BsmtCond_Gd                  0.000000e+00\n",
       "BldgType_TwnhsE              0.000000e+00\n",
       "BldgType_Twnhs              -0.000000e+00\n",
       "BldgType_Duplex             -0.000000e+00\n",
       "BldgType_2fmCon             -0.000000e+00\n",
       "BldgType_1Fam                0.000000e+00\n",
       "ExterQual_Fa                 0.000000e+00\n",
       "ExterCond_Fa                -0.000000e+00\n",
       "ExterQual_TA                -0.000000e+00\n",
       "Functional_Mod              -0.000000e+00\n",
       "GarageFinish_None           -0.000000e+00\n",
       "GarageFinish_Fin             0.000000e+00\n",
       "GarageCond_Po               -0.000000e+00\n",
       "GarageCond_None             -0.000000e+00\n",
       "GarageCond_Gd                0.000000e+00\n",
       "GarageCond_Fa               -0.000000e+00\n",
       "GarageCond_Ex                0.000000e+00\n",
       "Functional_Sev              -0.000000e+00\n",
       "Functional_Min2              0.000000e+00\n",
       "FireplaceQu_None            -0.000000e+00\n",
       "Functional_Min1              0.000000e+00\n",
       "Functional_Maj2             -0.000000e+00\n",
       "Functional_Maj1             -0.000000e+00\n",
       "Foundation_Wood             -0.000000e+00\n",
       "Foundation_Stone             0.000000e+00\n",
       "Foundation_Slab              0.000000e+00\n",
       "Foundation_CBlock           -0.000000e+00\n",
       "FireplaceQu_TA              -0.000000e+00\n",
       "GarageFinish_RFn            -0.000000e+00\n",
       "GarageFinish_Unf            -0.000000e+00\n",
       "GarageQual_Ex                0.000000e+00\n",
       "GarageQual_Fa               -0.000000e+00\n",
       "Heating_Wall                 0.000000e+00\n",
       "Heating_OthW                -0.000000e+00\n",
       "Heating_Grav                -0.000000e+00\n",
       "Heating_GasW                 0.000000e+00\n",
       "Heating_GasA                 0.000000e+00\n",
       "Heating_Floor               -0.000000e+00\n",
       "HalfBath_2                  -0.000000e+00\n",
       "HalfBath_0                  -0.000000e+00\n",
       "GarageType_None             -0.000000e+00\n",
       "GarageType_CarPort          -0.000000e+00\n",
       "GarageType_BuiltIn           0.000000e+00\n",
       "GarageType_Basment          -0.000000e+00\n",
       "GarageType_Attchd           -0.000000e+00\n",
       "GarageType_2Types           -0.000000e+00\n",
       "GarageQual_TA                0.000000e+00\n",
       "GarageQual_Po               -0.000000e+00\n",
       "GarageQual_Gd                0.000000e+00\n",
       "FireplaceQu_Po              -0.000000e+00\n",
       "YrSold_2010                 -0.000000e+00\n",
       "Exterior2nd_CBlock          -0.000000e+00\n",
       "Exterior2nd_AsbShng         -0.000000e+00\n",
       "Exterior1st_CBlock          -0.000000e+00\n",
       "Exterior2nd_HdBoard         -0.000000e+00\n",
       "Exterior2nd_CmentBd          0.000000e+00\n",
       "FireplaceQu_Fa              -0.000000e+00\n",
       "Exterior2nd_BrkFace         -0.000000e+00\n",
       "Exterior2nd_Brk Cmn         -0.000000e+00\n",
       "Exterior1st_AsphShn         -0.000000e+00\n",
       "Exterior2nd_AsphShn          0.000000e+00\n",
       "Exterior1st_WdShing          0.000000e+00\n",
       "Exterior2nd_Other           -0.000000e+00\n",
       "Exterior1st_VinylSd         -0.000000e+00\n",
       "Exterior1st_Stucco           0.000000e+00\n",
       "Exterior1st_BrkComm         -0.000000e+00\n",
       "Exterior1st_Stone            0.000000e+00\n",
       "Exterior1st_Plywood         -0.000000e+00\n",
       "Exterior1st_ImStucc         -0.000000e+00\n",
       "Exterior1st_HdBoard         -0.000000e+00\n",
       "Exterior1st_CemntBd          0.000000e+00\n",
       "Exterior2nd_MetalSd          0.000000e+00\n",
       "Exterior2nd_ImStucc          0.000000e+00\n",
       "Exterior2nd_Plywood         -0.000000e+00\n",
       "Exterior2nd_Wd Shng         -0.000000e+00\n",
       "FireplaceQu_Ex               0.000000e+00\n",
       "Exterior1st_AsbShng         -0.000000e+00\n",
       "Fence_None                   0.000000e+00\n",
       "Fence_MnWw                  -0.000000e+00\n",
       "Fence_MnPrv                  0.000000e+00\n",
       "Fence_GdPrv                  0.000000e+00\n",
       "Fence_GdWo                  -0.000000e+00\n",
       "Exterior2nd_Wd Sdng          0.000000e+00\n",
       "Exterior2nd_VinylSd         -0.000000e+00\n",
       "Exterior2nd_Stucco           0.000000e+00\n",
       "Exterior2nd_Stone            0.000000e+00\n",
       "BsmtExposure_No             -1.789030e-04\n",
       "BsmtCond_Fa                 -6.245991e-04\n",
       "MSSubClass_30               -8.287407e-04\n",
       "Exterior1st_Wd Sdng         -1.045455e-03\n",
       "LotConfig_Inside            -1.387029e-03\n",
       "MiscVal                     -2.443289e-03\n",
       "BsmtUnfSF                   -3.412652e-03\n",
       "SaleType_WD                 -3.986946e-03\n",
       "Foundation_BrkTil           -5.401002e-03\n",
       "oHouseStyle                 -6.810118e-03\n",
       "YrSold_2009                 -8.841903e-03\n",
       "Neighborhood_NWAmes         -1.000781e-02\n",
       "BsmtFullBath_0.0            -1.618491e-02\n",
       "BsmtFinType1_Unf            -1.669639e-02\n",
       "SaleCondition_Abnorml       -1.883348e-02\n",
       "KitchenAbvGr_2              -2.707554e-02\n",
       "CentralAir_N                -3.403050e-02\n",
       "MSZoning_C (all)            -9.682124e-02"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FI_lasso.sort_values(\"Feature Importance\",ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2Ljk5mcTERPz9/SlbtiwnT55k27ZtPPPMM8C1RHb06NE3vfG///47pUqVsh9HR0eTlJRE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Gw2ANLS0jAYDDe16riIiIiIiIiUT7UfsQ4ICODixYt07969xLa8vDy8vb0JCwvj0KFDREZGAvDE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/2C1L9//wIPSbteNGMtUnrKcXF2ynFxdspxcXbK8aJpxtqJPP/88xUdgoiIiIiIiBThhn0q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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x729e874550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FI_lasso[FI_lasso[\"Feature Importance\"]!=0].sort_values(\"Feature Importance\").plot(kind=\"barh\",figsize=(15,25))\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ __Based on the \"Feature Importance\" plot and other try-and-error, I decided to add some features to the pipeline.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "class add_feature(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self,additional=1):\n",
    "        self.additional = additional\n",
    "    \n",
    "    def fit(self,X,y=None):\n",
    "        return self\n",
    "    \n",
    "    def transform(self,X):\n",
    "        if self.additional==1:\n",
    "            X[\"TotalHouse\"] = X[\"TotalBsmtSF\"] + X[\"1stFlrSF\"] + X[\"2ndFlrSF\"]   \n",
    "            X[\"TotalArea\"] = X[\"TotalBsmtSF\"] + X[\"1stFlrSF\"] + X[\"2ndFlrSF\"] + X[\"GarageArea\"]\n",
    "            \n",
    "        else:\n",
    "            X[\"TotalHouse\"] = X[\"TotalBsmtSF\"] + X[\"1stFlrSF\"] + X[\"2ndFlrSF\"]   \n",
    "            X[\"TotalArea\"] = X[\"TotalBsmtSF\"] + X[\"1stFlrSF\"] + X[\"2ndFlrSF\"] + X[\"GarageArea\"]\n",
    "            \n",
    "            X[\"+_TotalHouse_OverallQual\"] = X[\"TotalHouse\"] * X[\"OverallQual\"]\n",
    "            X[\"+_GrLivArea_OverallQual\"] = X[\"GrLivArea\"] * X[\"OverallQual\"]\n",
    "            X[\"+_oMSZoning_TotalHouse\"] = X[\"oMSZoning\"] * X[\"TotalHouse\"]\n",
    "            X[\"+_oMSZoning_OverallQual\"] = X[\"oMSZoning\"] + X[\"OverallQual\"]\n",
    "            X[\"+_oMSZoning_YearBuilt\"] = X[\"oMSZoning\"] + X[\"YearBuilt\"]\n",
    "            X[\"+_oNeighborhood_TotalHouse\"] = X[\"oNeighborhood\"] * X[\"TotalHouse\"]\n",
    "            X[\"+_oNeighborhood_OverallQual\"] = X[\"oNeighborhood\"] + X[\"OverallQual\"]\n",
    "            X[\"+_oNeighborhood_YearBuilt\"] = X[\"oNeighborhood\"] + X[\"YearBuilt\"]\n",
    "            X[\"+_BsmtFinSF1_OverallQual\"] = X[\"BsmtFinSF1\"] * X[\"OverallQual\"]\n",
    "            \n",
    "            X[\"-_oFunctional_TotalHouse\"] = X[\"oFunctional\"] * X[\"TotalHouse\"]\n",
    "            X[\"-_oFunctional_OverallQual\"] = X[\"oFunctional\"] + X[\"OverallQual\"]\n",
    "            X[\"-_LotArea_OverallQual\"] = X[\"LotArea\"] * X[\"OverallQual\"]\n",
    "            X[\"-_TotalHouse_LotArea\"] = X[\"TotalHouse\"] + X[\"LotArea\"]\n",
    "            X[\"-_oCondition1_TotalHouse\"] = X[\"oCondition1\"] * X[\"TotalHouse\"]\n",
    "            X[\"-_oCondition1_OverallQual\"] = X[\"oCondition1\"] + X[\"OverallQual\"]\n",
    "            \n",
    "           \n",
    "            X[\"Bsmt\"] = X[\"BsmtFinSF1\"] + X[\"BsmtFinSF2\"] + X[\"BsmtUnfSF\"]\n",
    "            X[\"Rooms\"] = X[\"FullBath\"]+X[\"TotRmsAbvGrd\"]\n",
    "            X[\"PorchArea\"] = X[\"OpenPorchSF\"]+X[\"EnclosedPorch\"]+X[\"3SsnPorch\"]+X[\"ScreenPorch\"]\n",
    "            X[\"TotalPlace\"] = X[\"TotalBsmtSF\"] + X[\"1stFlrSF\"] + X[\"2ndFlrSF\"] + X[\"GarageArea\"] + X[\"OpenPorchSF\"]+X[\"EnclosedPorch\"]+X[\"3SsnPorch\"]+X[\"ScreenPorch\"]\n",
    "\n",
    "    \n",
    "            return X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ __By using a pipeline, you can quickily experiment different feature combinations.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline([\n",
    "    ('labenc', labelenc()),\n",
    "    ('add_feature', add_feature(additional=2)),\n",
    "    ('skew_dummies', skew_dummies(skew=1)),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ __Im my case, doing PCA is very important. It lets me gain a relatively big boost on leaderboard. At first I don't believe PCA can help me, but \n",
    "in retrospect, maybe the reason is that the features I built are highly correlated, and it leads to multicollinearity. PCA can decorrelate these features.__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ __So I'll use approximately the same dimension in PCA as  in the original data. Since the aim here is not deminsion reduction.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_pipe = pipe.fit_transform(full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2917, 426)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_pipe.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_train=train.shape[0]\n",
    "X = full_pipe[:n_train]\n",
    "test_X = full_pipe[n_train:]\n",
    "y= train.SalePrice\n",
    "\n",
    "X_scaled = scaler.fit(X).transform(X)\n",
    "y_log = np.log(train.SalePrice)\n",
    "test_X_scaled = scaler.transform(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=410)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_scaled=pca.fit_transform(X_scaled)\n",
    "test_X_scaled = pca.transform(test_X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1458, 410), (1459, 410))"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled.shape, test_X_scaled.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling & Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define cross validation strategy\n",
    "def rmse_cv(model,X,y):\n",
    "    rmse = np.sqrt(-cross_val_score(model, X, y, scoring=\"neg_mean_squared_error\", cv=5))\n",
    "    return rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ __We choose 13 models and use 5-folds cross-calidation to evaluate these models.__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Models include:\n",
    "\n",
    "+ LinearRegression\n",
    "+ Ridge\n",
    "+ Lasso\n",
    "+ Random Forrest\n",
    "+ Gradient Boosting Tree\n",
    "+ Support Vector Regression\n",
    "+ Linear Support Vector Regression\n",
    "+ ElasticNet\n",
    "+ Stochastic Gradient Descent\n",
    "+ BayesianRidge\n",
    "+ KernelRidge\n",
    "+ ExtraTreesRegressor\n",
    "+ XgBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [LinearRegression(),Ridge(),Lasso(alpha=0.01,max_iter=10000),RandomForestRegressor(),GradientBoostingRegressor(),SVR(),LinearSVR(),\n",
    "          ElasticNet(alpha=0.001,max_iter=10000),SGDRegressor(max_iter=1000,tol=1e-3),BayesianRidge(),KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5),\n",
    "          ExtraTreesRegressor(),XGBRegressor()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 1026870159.526766, 488528070.4534\n",
      "Ridge: 0.117596, 0.0091\n",
      "Lasso: 0.121474, 0.0060\n",
      "RF: 0.140764, 0.0052\n",
      "GBR: 0.124154, 0.0072\n",
      "SVR: 0.112727, 0.0047\n",
      "LinSVR: 0.121564, 0.0081\n",
      "Ela: 0.111113, 0.0059\n",
      "SGD: 0.159686, 0.0092\n",
      "Bay: 0.110577, 0.0060\n",
      "Ker: 0.109276, 0.0055\n",
      "Extra: 0.136668, 0.0073\n",
      "Xgb: 0.126614, 0.0070\n"
     ]
    }
   ],
   "source": [
    "names = [\"LR\", \"Ridge\", \"Lasso\", \"RF\", \"GBR\", \"SVR\", \"LinSVR\", \"Ela\",\"SGD\",\"Bay\",\"Ker\",\"Extra\",\"Xgb\"]\n",
    "for name, model in zip(names, models):\n",
    "    score = rmse_cv(model, X_scaled, y_log)\n",
    "    print(\"{}: {:.6f}, {:.4f}\".format(name,score.mean(),score.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ __Next we do some hyperparameters tuning. First define a gridsearch method.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "class grid():\n",
    "    def __init__(self,model):\n",
    "        self.model = model\n",
    "    \n",
    "    def grid_get(self,X,y,param_grid):\n",
    "        grid_search = GridSearchCV(self.model,param_grid,cv=5, scoring=\"neg_mean_squared_error\")\n",
    "        grid_search.fit(X,y)\n",
    "        print(grid_search.best_params_, np.sqrt(-grid_search.best_score_))\n",
    "        grid_search.cv_results_['mean_test_score'] = np.sqrt(-grid_search.cv_results_['mean_test_score'])\n",
    "        print(pd.DataFrame(grid_search.cv_results_)[['params','mean_test_score','std_test_score']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_iter': 10000, 'alpha': 0.0005} 0.111296607965\n",
      "                                 params  mean_test_score  std_test_score\n",
      "0  {'max_iter': 10000, 'alpha': 0.0003}         0.111869        0.001513\n",
      "1  {'max_iter': 10000, 'alpha': 0.0002}         0.112745        0.001753\n",
      "2  {'max_iter': 10000, 'alpha': 0.0004}         0.111463        0.001392\n",
      "3  {'max_iter': 10000, 'alpha': 0.0005}         0.111297        0.001339\n",
      "4  {'max_iter': 10000, 'alpha': 0.0007}         0.111538        0.001284\n",
      "5  {'max_iter': 10000, 'alpha': 0.0006}         0.111359        0.001315\n",
      "6  {'max_iter': 10000, 'alpha': 0.0009}         0.111915        0.001206\n",
      "7  {'max_iter': 10000, 'alpha': 0.0008}         0.111706        0.001229\n"
     ]
    }
   ],
   "source": [
    "grid(Lasso()).grid_get(X_scaled,y_log,{'alpha': [0.0004,0.0005,0.0007,0.0006,0.0009,0.0008],'max_iter':[10000]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 60} 0.110201749615\n",
      "          params  mean_test_score  std_test_score\n",
      "0  {'alpha': 35}         0.110375        0.001268\n",
      "1  {'alpha': 40}         0.110305        0.001249\n",
      "2  {'alpha': 45}         0.110258        0.001235\n",
      "3  {'alpha': 50}         0.110227        0.001223\n",
      "4  {'alpha': 55}         0.110209        0.001213\n",
      "5  {'alpha': 60}         0.110202        0.001205\n",
      "6  {'alpha': 65}         0.110203        0.001198\n",
      "7  {'alpha': 70}         0.110212        0.001192\n",
      "8  {'alpha': 80}         0.110247        0.001184\n",
      "9  {'alpha': 90}         0.110301        0.001178\n"
     ]
    }
   ],
   "source": [
    "grid(Ridge()).grid_get(X_scaled,y_log,{'alpha':[35,40,45,50,55,60,65,70,80,90]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'gamma': 0.0004, 'C': 13, 'kernel': 'rbf', 'epsilon': 0.009} 0.108231959163\n",
      "                                                           params  mean_test_score  std_test_score\n",
      "0   {'gamma': 0.0003, 'C': 11, 'kernel': 'rbf', 'epsilon': 0.008}         0.108661        0.001553\n",
      "1   {'gamma': 0.0004, 'C': 11, 'kernel': 'rbf', 'epsilon': 0.008}         0.108312        0.001609\n",
      "2   {'gamma': 0.0003, 'C': 11, 'kernel': 'rbf', 'epsilon': 0.009}         0.108650        0.001555\n",
      "3   {'gamma': 0.0004, 'C': 11, 'kernel': 'rbf', 'epsilon': 0.009}         0.108297        0.001607\n",
      "4   {'gamma': 0.0003, 'C': 12, 'kernel': 'rbf', 'epsilon': 0.008}         0.108604        0.001577\n",
      "5   {'gamma': 0.0004, 'C': 12, 'kernel': 'rbf', 'epsilon': 0.008}         0.108280        0.001618\n",
      "6   {'gamma': 0.0003, 'C': 12, 'kernel': 'rbf', 'epsilon': 0.009}         0.108587        0.001578\n",
      "7   {'gamma': 0.0004, 'C': 12, 'kernel': 'rbf', 'epsilon': 0.009}         0.108249        0.001621\n",
      "8   {'gamma': 0.0003, 'C': 13, 'kernel': 'rbf', 'epsilon': 0.008}         0.108562        0.001601\n",
      "9   {'gamma': 0.0004, 'C': 13, 'kernel': 'rbf', 'epsilon': 0.008}         0.108248        0.001615\n",
      "10  {'gamma': 0.0003, 'C': 13, 'kernel': 'rbf', 'epsilon': 0.009}         0.108555        0.001602\n",
      "11  {'gamma': 0.0004, 'C': 13, 'kernel': 'rbf', 'epsilon': 0.009}         0.108232        0.001621\n",
      "12  {'gamma': 0.0003, 'C': 14, 'kernel': 'rbf', 'epsilon': 0.008}         0.108497        0.001617\n",
      "13  {'gamma': 0.0004, 'C': 14, 'kernel': 'rbf', 'epsilon': 0.008}         0.108242        0.001611\n",
      "14  {'gamma': 0.0003, 'C': 14, 'kernel': 'rbf', 'epsilon': 0.009}         0.108482        0.001618\n",
      "15  {'gamma': 0.0004, 'C': 14, 'kernel': 'rbf', 'epsilon': 0.009}         0.108240        0.001621\n",
      "16  {'gamma': 0.0003, 'C': 15, 'kernel': 'rbf', 'epsilon': 0.008}         0.108474        0.001629\n",
      "17  {'gamma': 0.0004, 'C': 15, 'kernel': 'rbf', 'epsilon': 0.008}         0.108248        0.001609\n",
      "18  {'gamma': 0.0003, 'C': 15, 'kernel': 'rbf', 'epsilon': 0.009}         0.108444        0.001633\n",
      "19  {'gamma': 0.0004, 'C': 15, 'kernel': 'rbf', 'epsilon': 0.009}         0.108251        0.001617\n"
     ]
    }
   ],
   "source": [
    "grid(SVR()).grid_get(X_scaled,y_log,{'C':[11,12,13,14,15],'kernel':[\"rbf\"],\"gamma\":[0.0003,0.0004],\"epsilon\":[0.008,0.009]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kernel Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'coef0': 0.8, 'kernel': 'polynomial', 'alpha': 0.2, 'degree': 3} 0.108269736148\n",
      "                                                               params  mean_test_score  std_test_score\n",
      "0   {'coef0': 0.8, 'kernel': 'polynomial', 'alpha': 0.2, 'degree': 3}         0.108270        0.001209\n",
      "1     {'coef0': 1, 'kernel': 'polynomial', 'alpha': 0.2, 'degree': 3}         0.108509        0.001243\n",
      "2   {'coef0': 1.2, 'kernel': 'polynomial', 'alpha': 0.2, 'degree': 3}         0.108942        0.001286\n",
      "3   {'coef0': 0.8, 'kernel': 'polynomial', 'alpha': 0.3, 'degree': 3}         0.108399        0.001189\n",
      "4     {'coef0': 1, 'kernel': 'polynomial', 'alpha': 0.3, 'degree': 3}         0.108278        0.001210\n",
      "5   {'coef0': 1.2, 'kernel': 'polynomial', 'alpha': 0.3, 'degree': 3}         0.108505        0.001245\n",
      "6   {'coef0': 0.8, 'kernel': 'polynomial', 'alpha': 0.4, 'degree': 3}         0.108762        0.001181\n",
      "7     {'coef0': 1, 'kernel': 'polynomial', 'alpha': 0.4, 'degree': 3}         0.108299        0.001191\n",
      "8   {'coef0': 1.2, 'kernel': 'polynomial', 'alpha': 0.4, 'degree': 3}         0.108363        0.001219\n",
      "9   {'coef0': 0.8, 'kernel': 'polynomial', 'alpha': 0.5, 'degree': 3}         0.109242        0.001180\n",
      "10    {'coef0': 1, 'kernel': 'polynomial', 'alpha': 0.5, 'degree': 3}         0.108430        0.001180\n",
      "11  {'coef0': 1.2, 'kernel': 'polynomial', 'alpha': 0.5, 'degree': 3}         0.108345        0.001201\n"
     ]
    }
   ],
   "source": [
    "param_grid={'alpha':[0.2,0.3,0.4,0.5], 'kernel':[\"polynomial\"], 'degree':[3],'coef0':[0.8,1,1.2]}\n",
    "grid(KernelRidge()).grid_get(X_scaled,y_log,param_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ElasticNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_iter': 10000, 'alpha': 0.005, 'l1_ratio': 0.08} 0.111171351585\n",
      "                                                    params  mean_test_score  std_test_score\n",
      "0   {'max_iter': 10000, 'alpha': 0.0005, 'l1_ratio': 0.08}         0.116552        0.002026\n",
      "1    {'max_iter': 10000, 'alpha': 0.0005, 'l1_ratio': 0.1}         0.116024        0.002007\n",
      "2    {'max_iter': 10000, 'alpha': 0.0005, 'l1_ratio': 0.3}         0.113118        0.001819\n",
      "3    {'max_iter': 10000, 'alpha': 0.0005, 'l1_ratio': 0.5}         0.112099        0.001597\n",
      "4    {'max_iter': 10000, 'alpha': 0.0005, 'l1_ratio': 0.7}         0.111625        0.001435\n",
      "5   {'max_iter': 10000, 'alpha': 0.0008, 'l1_ratio': 0.08}         0.114762        0.001937\n",
      "6    {'max_iter': 10000, 'alpha': 0.0008, 'l1_ratio': 0.1}         0.114250        0.001904\n",
      "7    {'max_iter': 10000, 'alpha': 0.0008, 'l1_ratio': 0.3}         0.112062        0.001596\n",
      "8    {'max_iter': 10000, 'alpha': 0.0008, 'l1_ratio': 0.5}         0.111416        0.001384\n",
      "9    {'max_iter': 10000, 'alpha': 0.0008, 'l1_ratio': 0.7}         0.111319        0.001325\n",
      "10   {'max_iter': 10000, 'alpha': 0.004, 'l1_ratio': 0.08}         0.111278        0.001382\n",
      "11    {'max_iter': 10000, 'alpha': 0.004, 'l1_ratio': 0.1}         0.111209        0.001326\n",
      "12    {'max_iter': 10000, 'alpha': 0.004, 'l1_ratio': 0.3}         0.112483        0.001160\n",
      "13    {'max_iter': 10000, 'alpha': 0.004, 'l1_ratio': 0.5}         0.113876        0.001184\n",
      "14    {'max_iter': 10000, 'alpha': 0.004, 'l1_ratio': 0.7}         0.115219        0.001241\n",
      "15   {'max_iter': 10000, 'alpha': 0.005, 'l1_ratio': 0.08}         0.111171        0.001312\n",
      "16    {'max_iter': 10000, 'alpha': 0.005, 'l1_ratio': 0.1}         0.111192        0.001277\n",
      "17    {'max_iter': 10000, 'alpha': 0.005, 'l1_ratio': 0.3}         0.112983        0.001159\n",
      "18    {'max_iter': 10000, 'alpha': 0.005, 'l1_ratio': 0.5}         0.114734        0.001214\n",
      "19    {'max_iter': 10000, 'alpha': 0.005, 'l1_ratio': 0.7}         0.116235        0.001247\n"
     ]
    }
   ],
   "source": [
    "grid(ElasticNet()).grid_get(X_scaled,y_log,{'alpha':[0.0005,0.0008,0.004,0.005],'l1_ratio':[0.08,0.1,0.3,0.5,0.7],'max_iter':[10000]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensemble Methods "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weight Average"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ __Average base models according to their weights.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AverageWeight(BaseEstimator, RegressorMixin):\n",
    "    def __init__(self,mod,weight):\n",
    "        self.mod = mod\n",
    "        self.weight = weight\n",
    "        \n",
    "    def fit(self,X,y):\n",
    "        self.models_ = [clone(x) for x in self.mod]\n",
    "        for model in self.models_:\n",
    "            model.fit(X,y)\n",
    "        return self\n",
    "    \n",
    "    def predict(self,X):\n",
    "        w = list()\n",
    "        pred = np.array([model.predict(X) for model in self.models_])\n",
    "        # for every data point, single model prediction times weight, then add them together\n",
    "        for data in range(pred.shape[1]):\n",
    "            single = [pred[model,data]*weight for model,weight in zip(range(pred.shape[0]),self.weight)]\n",
    "            w.append(np.sum(single))\n",
    "        return w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso = Lasso(alpha=0.0005,max_iter=10000)\n",
    "ridge = Ridge(alpha=60)\n",
    "svr = SVR(gamma= 0.0004,kernel='rbf',C=13,epsilon=0.009)\n",
    "ker = KernelRidge(alpha=0.2 ,kernel='polynomial',degree=3 , coef0=0.8)\n",
    "ela = ElasticNet(alpha=0.005,l1_ratio=0.08,max_iter=10000)\n",
    "bay = BayesianRidge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# assign weights based on their gridsearch score\n",
    "w1 = 0.02\n",
    "w2 = 0.2\n",
    "w3 = 0.25\n",
    "w4 = 0.3\n",
    "w5 = 0.03\n",
    "w6 = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "weight_avg = AverageWeight(mod = [lasso,ridge,svr,ker,ela,bay],weight=[w1,w2,w3,w4,w5,w6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.10424903,  0.10955878,  0.11835673,  0.10016298,  0.10609548]),\n",
       " 0.10768459878025709)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_cv(weight_avg,X_scaled,y_log),  rmse_cv(weight_avg,X_scaled,y_log).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ __But if we average only two best models, we gain better cross-validation score.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "weight_avg = AverageWeight(mod = [svr,ker],weight=[0.5,0.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.10273174,  0.1093225 ,  0.11762506,  0.09857604,  0.10516214]),\n",
       " 0.10668349587194834)"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_cv(weight_avg,X_scaled,y_log),  rmse_cv(weight_avg,X_scaled,y_log).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stacking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ __Aside from normal stacking, I also add the \"get_oof\" method, because later I'll combine features generated from stacking and original features.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "class stacking(BaseEstimator, RegressorMixin, TransformerMixin):\n",
    "    def __init__(self,mod,meta_model):\n",
    "        self.mod = mod\n",
    "        self.meta_model = meta_model\n",
    "        self.kf = KFold(n_splits=5, random_state=42, shuffle=True)\n",
    "        \n",
    "    def fit(self,X,y):\n",
    "        self.saved_model = [list() for i in self.mod]\n",
    "        oof_train = np.zeros((X.shape[0], len(self.mod)))\n",
    "        \n",
    "        for i,model in enumerate(self.mod):\n",
    "            for train_index, val_index in self.kf.split(X,y):\n",
    "                renew_model = clone(model)\n",
    "                renew_model.fit(X[train_index], y[train_index])\n",
    "                self.saved_model[i].append(renew_model)\n",
    "                oof_train[val_index,i] = renew_model.predict(X[val_index])\n",
    "        \n",
    "        self.meta_model.fit(oof_train,y)\n",
    "        return self\n",
    "    \n",
    "    def predict(self,X):\n",
    "        whole_test = np.column_stack([np.column_stack(model.predict(X) for model in single_model).mean(axis=1) \n",
    "                                      for single_model in self.saved_model]) \n",
    "        return self.meta_model.predict(whole_test)\n",
    "    \n",
    "    def get_oof(self,X,y,test_X):\n",
    "        oof = np.zeros((X.shape[0],len(self.mod)))\n",
    "        test_single = np.zeros((test_X.shape[0],5))\n",
    "        test_mean = np.zeros((test_X.shape[0],len(self.mod)))\n",
    "        for i,model in enumerate(self.mod):\n",
    "            for j, (train_index,val_index) in enumerate(self.kf.split(X,y)):\n",
    "                clone_model = clone(model)\n",
    "                clone_model.fit(X[train_index],y[train_index])\n",
    "                oof[val_index,i] = clone_model.predict(X[val_index])\n",
    "                test_single[:,j] = clone_model.predict(test_X)\n",
    "            test_mean[:,i] = test_single.mean(axis=1)\n",
    "        return oof, test_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ __Let's first try it out ! It's a bit slow to run this method, since the process is quite compliated. __"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# must do imputer first, otherwise stacking won't work, and i don't know why.\n",
    "a = Imputer().fit_transform(X_scaled)\n",
    "b = Imputer().fit_transform(y_log.values.reshape(-1,1)).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "stack_model = stacking(mod=[lasso,ridge,svr,ker,ela,bay],meta_model=ker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.10329472,  0.10976327,  0.1172047 ,  0.09831137,  0.1043045 ]),\n",
       " 0.1065757122654423)"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(rmse_cv(stack_model,a,b))\n",
    "print(rmse_cv(stack_model,a,b).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ __Next we extract the features generated from stacking, then combine them with original features.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_stack, X_test_stack = stack_model.get_oof(a,b,test_X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1458, 6), (1458, 410))"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_stack.shape, a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_add = np.hstack((a,X_train_stack))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_add = np.hstack((test_X_scaled,X_test_stack))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1458, 416), (1459, 416))"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_add.shape, X_test_add.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.09871787  0.10513473  0.11222932  0.09395028  0.09909121]\n",
      "0.101824682747\n"
     ]
    }
   ],
   "source": [
    "print(rmse_cv(stack_model,X_train_add,b))\n",
    "print(rmse_cv(stack_model,X_train_add,b).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ __You can even do parameter tuning for your meta model after you get \"X_train_stack\", or do it after combining with the original features. but that's a lot of work too !__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is the final model I use\n",
    "stack_model = stacking(mod=[lasso,ridge,svr,ker,ela,bay],meta_model=ker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stacking(meta_model=KernelRidge(alpha=0.2, coef0=0.8, degree=3, gamma=None, kernel='polynomial',\n",
       "      kernel_params=None),\n",
       "     mod=[Lasso(alpha=0.0005, copy_X=True, fit_intercept=True, max_iter=10000,\n",
       "   normalize=False, positive=False, precompute=False, random_state=None,\n",
       "   selection='cyclic', tol=0.0001, warm_start=False), Ridge(alpha=60, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=No...True, lambda_1=1e-06, lambda_2=1e-06, n_iter=300,\n",
       "       normalize=False, tol=0.001, verbose=False)])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stack_model.fit(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = np.exp(stack_model.predict(test_X_scaled))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "result=pd.DataFrame({'Id':test.Id, 'SalePrice':pred})\n",
    "result.to_csv(\"submission.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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